Dynamic Interaction Between Savings, Investment and Economic Growth in Somalia: A Vector Autoregressive (Var) Approach

Dynamic Interaction Between Savings, Investment and Economic Growth in Somalia: A Vector Autoregressive (Var) Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dynamic Interaction Between Savings, Investment and Economic Growth in Somalia: A Vector Autoregressive (Var) Approach Abdishakur Mohamud Ahmed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6434327/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigate the dynamic interaction between saving, investment, and economic growth in Somalia from 1989 to 2022 using a Vector Autoregressive (VAR) approach. The findings indicate that a one-percentage point increase in previous LGDI leads to a 0.8457% increase in current LGDI, while LGDP causes a drop in LGDI. Previous savings levels have unfavorable effects on current investment, with LGDP values increasing by 0.8709% when their previous value increases by 1%. The model's explanatory power is solid, with high F-statistics and statistical significance. The model's effectiveness is supported by Akaike and Schwarz criteria and low residual covariance values. While Granger causality test found predictive relationships between gross domestic investment (LGDI), gross domestic product (LGDP), and gross domestic savings (LGDS). Past savings data are crucial for anticipating future investment, but no significant correlations were found. Based on the empirical evidence, the study provides several policy impilications, including financial literacy programs, promoting political stability, and increase investments in education and healthcare. Saving Investment Economic growth Vector autoregressive (VAR) approach Figures Figure 1 Figure 2 1. Introduction Economic stability, capital accumulation, and long-term development depend on the dynamic interplay between savings, investment, and global economic growth [ 1 ]. Investment necessitates accumulated capital, and this process fosters economic progress by enhancing production performance and expansion [ 2 ]. According to [ 3 ], the economic environment stabilizes more effectively when inflation is low and interest rates are stable, as these conditions promote increased savings and hence enhance investments that drive growth. However, the relationship between saving, investment, and economic growth encounters several challenges, as fluctuating interest rates hinder borrowers and investors, whereas rampant inflation diminishes the value of savings [ 4 ]. External economic downturns diminish investor confidence, resulting in disrupted capital flows [ 5 ]. The global correlation between savings, economic growth, and investment is complicated by external economic disruptions such as the COVID-19 pandemic and escalating geopolitical tensions. During the pandemic, worldwide savings rates significantly declined as people relied on their savings to endure income decreases; concurrently, developing countries experienced a 20% decrease in investment capital from 2020 [ 6 ]. Global investment growth, measured as annual growth in real gross fixed capital formation, has decreased from 3.3% in 2022 to only 1.9% in 2023. While mild increase for 2024; nonetheless, investment rates will remain below the trend rates of 2011–2019 at 4% [ 7 ]. Over the past two decades, economic advancements in sub-Saharan Africa have stemmed from substantial expenditures in infrastructure and natural resources, which are uneven, particularly among oil-exporting countries, as noted by the [ 8 ]. The countries hardest hit by fluctuations in oil prices, especially Angola, Nigeria, and the CEMAC member states, have to struggle with significant declines in budget revenues and balance-of-payments problems [ 9 ]. According to the [ 10 ], two African countries, notably Botswana and Ghana, demonstrated substantial savings rate gains that fostered investment and economic expansion. This area faces hurdles that dissuade investors both at home and abroad, thereby decreasing its capacity to achieve sustainable economic development. Unpredictable power supply, along with poor transportation networks, elevates corporate operating expenditures, which decreases investment opportunities for FDI in the area [ 11 ]. However, Sub-Saharan African economies have historically low savings rates, averaging around 17% of GDP, whereas the world standard is 25%. Low savings levels hinder local investment options because many countries must accept external financial assistance that includes severe requirements and boosts debt levels [ 12 ]. Somalia needs well-established links between savings and investment to achieve economic growth, which leads to greater economic development. The country suffers considerable challenges, such as civil war coupled with political instability and weak governance, which impede both investment and economic expansion [ 13 ]. Somalia's financial system has become more robust in recent years, while attracting investments that mostly focus on the telecom and construction fields [ 14 ]. The economic prosperity of Somalia hinges mainly on investments made in education and healthcare. The school system has suffered greatly because of the civil war; however, education expenditure remains crucial for generating skilled individuals [ 15 ]. The real GDP growth rate rose from 2.4 percent in 2022 to 3.3 percent in 2021 as Somalia began overcoming the COVID-19 pandemic effects that caused negative growth in 2020 [ 16 ]. According to [ 17 ], the nation's investment levels remain sluggish because the average gross capital creation reaches just 13–14% of GDP from 2012 to 2020. The economic status of Somalia has been damaged by continuous warfare paired with political instability and low institutional capabilities, so it remains unable to develop savings while attracting investment. Somalia maintains one of the world's lowest savings rates because poverty, along with insufficient banking institutions and informal economic structures, affects more than 5% of the GDP [ 18 ]. The scarcity of domestic savings has hindered Somalia from developing its capital accumulation, which has led the nation to depend largely on foreign aid and remittances to fund its basic requirements and reconstruction endeavors. More than 30 percent of the national GDP comes from remittances, yet these funds mostly support household expenditure, which precludes sustained development [ 19 ]. Somalia presents hurdles to investment because of security threats, poor governance systems, and limited infrastructure that make both indigenous and foreign investors apprehensive. Insufficient regulatory stability, along with inactive financial institutions, provides considerable investment obstacles to enterprises working in the agricultural, industrial, and energy sectors. The absence of credit and collateral infrastructure prohibits firms from establishing or expanding; thus, they cannot create jobs while fighting poverty [ 19 ]. Although the country has various hurdles, Somalia has multiple promising economic growth opportunities within its cattle and fisheries sectors, alongside renewable energy development. Somalia aims to gain from its geographic position between the Gulf of Aden and the Indian Ocean by offering new commercial opportunities for port development and logistical infrastructure. The World Bank support the possibility of improving economic stability owing to state-building progress and federal government development [ 18 ]. This study seeks to address this gap by examining the dynamic interactions among investment, savings, and economic growth in Somalia from 1989 to 2022. This study utilizes the vector autoregressive technique (VAR), which is most suitable because of the behavior of the variables involved. This study employs several econometric methods to ensure robust and reliable results. These include unit root tests, Johansen cointegration method, limits testing, Granger causality analysis, impulse response functions, and variance decomposition analysis. Additionally, the study provides data that might lead to specific strategies for solving the difficulties faced by barriers to investment, elevated unemployment, and overall economic stability in Somalia. The subsequent sections of the study are structured as follows: The second portion covers the empirical literature, and section presents the theoretical framework, data sources, model definition, and econometric technique. Section 4 presents the empirical analysis and debates, and the concluding section summarizes the study and provides policy recommendations. 2. Literature Review In recent decades, literature has consistently examined the relationship between savings, investment, and economic growth. [ 20 ] analyzed the dynamic interplay among saving, investment, and economic growth in Ethiopia from 1992/1993 to 2019/20 utilizing quarterly data. The findings from the Vector Error Correction Model (VECM) demonstrate a positive long-term link among savings, investment, and economic growth. The Granger causality test indicates that domestic investment and real GDP cause domestic savings, whereas domestic investment causes economic growth. These results underscore the significance of business savings in stimulating economic growth. Additionally, [ 21 ] investigated the correlation between domestic savings and sustainable economic growth in South Africa from 1990 to 2023 utilizing the ARDL framework. The results demonstrate that corporate savings significantly impact sustainable economic growth, suggesting that effective mobilization of corporate savings is essential for promoting economic stability and growth. Numerous studies have identified a positive correlation between savings, investment, and economic growth [ 22 ]; [ 23 ]; [ 24 ]. While data from Nigeria indicated considerable connectivity between the three variables, it also indicated that GDP had a larger influence on both gross national saving and gross capital formation, [ 25 ]. Furthermore, studies in Nepal found a positive association between gross domestic product and gross domestic savings in the long run but a negative correlation in the short run. In addition, gross domestic product maintained a positive link with gross capital formation in both the long and short term [ 26 ]. Several studies have found a complex relationship between savings and economic growth. [ 27 ] studied the causal relationship between gross domestic saving and economic growth in six sub-Saharan nations from 1981 to 2014 using the ARDL and Toda-Yamamoto techniques. They observed unidirectional causality economic growth to gross domestic saving in Ghan and Burkina-Faso, but gross domestic saving Granger causes in economic growth in Liberia, Niger, and Sierra Leone. No causal links have been found in Nigeria. In contrast, [ 28 ] focus on Nigeria from 1981 to 2014, utilizing the Vector Autoregressive (VAR) approach. Their funding revealed a unidirectional causal association between gross domestic product (GDP) and gross domestic savings (GDS), indicating that economic expansion affects savings. [ 29 ] studied the relationship between economic development and domestic saving in Pakistan from 1971 to 2007 using ARDL and Granger causality analysis. They observed a long-run link between economic growth and domestic savings, demonstrating that economic expansion leads to more savings, whereas there is no reciprocal causality from saving to growth. In addition, we analyze the causal association between savings and economic development in both advanced and emerging economies from 1980 to 2010. They observed a unidirectional causal relationship, in which economic expansion stimulates savings in both types of economies, but not vice versa [ 30 ]. Similarly, studies in Nepal from 1974/75 to 2018/19 using the ARDL technique demonstrated unidirectional causality from economic growth to saving, showing that economic expansion stimulates saving but not vice versa. [ 31 ] studied the causal relationship between household saving and economic growth in Cambodia from 1989 to 2012 using the Granger causality test. They observed no causal association between domestic saving and economic growth; specifically, domestic saving does not Granger-cause economic growth, and economic growth does not Granger-cause savings. Using the Vector Error Correction Model (VECM), [ 32 ] evaluated time-series data from 1986 to 2018 and studied the link between saving and economic growth. Furthermore, they observe a negative correlation between savings and economic growth. Literature suggests that the degree of investment plays a significant role in driving economic growth. [ 33 ] evaluated the long-term association between investment and economic growth in OECD countries. Their investigation found a strong correlation between domestic investments and economic growth. Using the panel CS-ARDL model in North African countries, [ 34 ] found a favorable impact of domestic investment on economic growth in the long and short run. In addition, [ 35 ], using the Vector Error Correction Model (VECM), showed that economic growth has no influence on investment in the short run, but long-run economic growth has a positive impact on investment. [ 36 ] studied the link between gross capital formation and economic growth in Zimbabwe, using the autoregressive approach (ARDL). They discovered that gross capital formation was good, but not considerable, economic growth. However, the literature reveals that saving has a complex relationship with economic growth. [ 37 ] studied the relationship between saving and investment evidence from Jordan, using the autoregressive approach (ARDL). They observe a positive connection between savings and investment. This study reveals essential domestic savings in boosting investment and growth [ 38 ]; [ 39 ]; and [ 40 ]. They observe unidirectional causality from savings to investment. Similarly, both studies [ 41 ] and [ 42 ], they find unidirectional causation from saving to investment, demonstrating a lack of long-run association between the two variables, which implies considerable capital mobility. On the other hand, [ 43 ] studied the long-run link between saving and investment in Ethiopia. They discover no causation between savings and investment in either way. In addition, [ 44 ] they identified a unidirectional causal relationship from investment to saving and economic growth. This study criticizes the common macroeconomic assumption that “saving equal’s investment,” suggesting that this approach leads to serious errors in economic analysis [ 45 ]. They concluded that the U.S. economy has had a negative saving rate for over three decades, which has resulted in a stagnating economy defined by poor productivity and high debt levels. In summary, the literature illustrates the complex link between savings, investment, and economic growth. This highlights the significance of considering multiple econometric techniques to understand the dynamics of these relationships. Despite numerous empirical studies on savings, investment, and economic growth in recent years, the literature has generally disregarded the interconnectedness of these variables. This study analyzes the causal linkages between savings, investment, and economic growth. Unlike prior studies, our research focuses on the specific economic context of Somalia by applying a comprehensive set of econometric approaches to discover the complicated association between these crucial variables. 3. Methodology and Data 3.1. Sampling and data source Data from 1980 to 2022 has been used to develop a time series model for the study. The data was acquired from the Organization of Islamic Cooperation (OIC-SESRIC). The variables used in this study include gross domestic output, gross domestic investment, and gross domestic saving, as indicated in Table 1 . Table 1 Variables and data source Variables Proxy Measurement Source Economic growth GDP GDP per Capita (Constant 2015 Prices) Sesric Gross domestic investment GDI Gross Capital Formation, Constant 2015 Prices Sesric Gross domestic saving GDS Gross Domestic Savings, Constant 2015 Prices Sesric The trend analysis of Somalia's economic variables—Gross Domestic Product (GDP), Gross Domestic Investment (GDI), and Gross Domestic Saving (GDS)—from 1989 to 2022 indicates important patterns across the years. Starting in 1989, the GDP was reported at around 5.56 and exhibited a steady climb in the next years to reach roughly 5.89 by 2011. This upward trend shows a period of economic improvement, despite minor fluctuations throughout the early 1990s, particularly during the civil instability in 1991–1992, where GDP experienced slight drops but steadied subsequently. Especially, the GDP peaked in 2019 at 6.34 before considerably dropping in 2020 and 2021, showing some economic problems in recent times. On the contrary, hand, GDI showed a more continuous upward trend across the years, commencing at 19.85 in 1989 and slowly rising to 21.49 by 2022. This demonstrates a constant economic investment, which is extremely crucial for encouraging development and advancement. The GDI's durability implies that there has been a dedication to improve investment levels despite economic constraints. Finally, GDS demonstrated more flux. Beginning at 4.05 in 1989, it kept reasonably constant until 2012, when it clearly dropped to -0.92, signaling a time of dissaving. This was followed by a recovery phase, with GDS growing to 2.64 in 2021 before decreasing slightly to 2.37 in 2022. The swings in savings could reflect variations in economic stability and household confidence, highlighting the necessity of policies that encourage saving behavior. 3.2. Unit root test As the model applied in this study involves a temporal trend (t), nonstationarity suggests that a normal regression analysis will yield misleading and untrustworthy results. Therefore, it is necessary to check whether the time-series variables are integrated of order one before executing the VAR test. To do this, the augmented Dickey (ADF) unit root test was applied. The alternative hypothesis (H1) contends that the series is stationary, whereas the null hypothesis (H0) asserts that the series has a unit root. 3.3. Vector autoregressive approach The VAR models adopted to examine the dynamic interaction among the variables used in this study are expressed as follows: $$\:{LGDI}_{t}={\propto\:}_{1}+\sum\:_{j=1}^{n}{\beta\:}_{j}{LGDI}_{t-j}+\sum\:_{j=1}^{n}{\theta\:}_{j}{LGDP}_{t-j}+\sum\:_{j=1}^{n}{\gamma\:}_{j}{LGDS}_{t-j}+{\mu\:}_{1t}$$ $$\:{LGDP}_{t}={\propto\:}_{2}+\sum\:_{j=1}^{n}{\theta\:}_{j}{LGDP}_{t-j}+\sum\:_{j=1}^{n}{\beta\:}_{j}{LGDI}_{t-j}+\sum\:_{j=1}^{n}{\gamma\:}_{j}{LGDS}_{t-j}+{\mu\:}_{2t}$$ $$\:{LGDS}_{t}={\propto\:}_{3}+\sum\:_{j=1}^{n}{\gamma\:}_{j}{LGDS}_{t-j}+\sum\:_{j=1}^{n}{\beta\:}_{j}{LGDP}_{t-j}+\sum\:_{j=1}^{n}{\theta\:}_{j}{LGDI}_{t-j}+{\mu\:}_{3t}$$ The parameters are denoted by the coefficients β, θ, and γ in the VAR equation above, where n denotes the lag order. L represents logarithm, µ is the stochastic error term termed impulses, innovations, or shocks in VAR, and t is the current time. The first stage in estimating the VAR model is the application of a root test that validates the condition of stationarity for each chosen endogenous variable, the second step lag order selection, the third step Johansen cointegration test, and the fourth step estimation of VAR. 4. Result and discussion 4.1 Descriptive statistics The preliminary summary statistics and correlation matrix for the major factors describing the raw data are provided in Table 2 using Eviews 12. According to Table 2 , the mean for gross domestic savings (GDS) is the highest at 24.81, whereas the mean for Gross Domestic Product is the lowest at 5.72. Notably, GDS has its highest maximum value of 24.83, whereas Gross Domestic Product has the lowest minimum of 5.38. One notable result is that the Gross Domestic Investment (GDI) has the greatest standard deviation (0.56), indicating a greater range of values around its mean. The Jarque-Bera test results showed that the data were identically and normally distributed. Panel B presents the correlation matrix of the variables. Except for gross domestic savings, all the variables suggest a positive connection with GDI. There is a negative correlation between gross domestic saving and GDI. Similarly, all parameters, excluding unemployment, suggest a positive connection with GDI. A positive correlation exists between the gross domestic product (GDP) and gross domestic investment (GDI). Table 2 Descriptive statistics Panel A: Descriptive LGDI LGDP LGDS Mean 19.92 5.72 24.81 Median 19.75 5.62 24.82 Maximum 21.49 6.34 24.83 Minimum 19.35 5.38 24.75 Std. Dev. 0.56 0.31 0.022 Skewness 1.26 0.90 -1.11 Kurtosis 3.61 2.44 2.68 Jarque-Bera 12.14 6.38 8.99 Probability 0.0023 0.041 0.011 Observations 43 43 43 Panel B: Correlation LGDI LGDP LGDS LGDI LGDP 1 0.936 1 LGDS -0.945 -0.959 1 4.2. Lag length criteria This study employs the majority technique to determine the ideal lag length. The five criteria studied were the sequential modified LR test statistic (LR), final prediction error (FPE), Akaike information criterion (AIC), Schwarz information criterion (SIC), and Hannan-Quinn information criterion (HQ), as shown in Table 4 . All five considerations show that the optimal lag length is one. 4.3. Unit root test This study adopts a model with a temporal trend (t) for which non-stationarity could lead to erroneous conclusions from a simple regression analysis. It is necessary to check whether the time-series variables reflect first-order integration before executing the vector autoregressive (VAR) model. To this end, this study employs the ADF unit root test. In the ADF test, the alternative hypothesis implies the absence of a unit root problem, whereas the null hypothesis suggests the existence of one. Therefore, if the variable’s t-statistic is larger than the essential t-value, we agree that the data are stationary and reject the null hypothesis of nonstationarity. Conversely, if the t-statistic is less than the critical t-value, the nonstationarity null hypothesis cannot be ruled out. Essentially, these tests assist in identifying whether the data exhibit constant mean and variance over time. Moreover, all variables are nonstationary, as demonstrated by the results of the ADF unit root test shown in Table 3 . Using nonstationary variables can lead to erroneous results with serious repercussions. The results in Table 3 demonstrate that all the variables are stationary at the initial difference. 4.4. Cointegration test The long-term association between time-series variables can be statistically established by applying the Johansen cointegration max-eigenvalue and trace tests. These tests consider the null hypothesis that there are no co-integrating vectors, and calculate the rank of the co-integration matrix. The Johansen Max-Eigenvalue and trace test results, presented in Table 5 , provide evidence of no co-integrating equations among the variables in Somalia. This result was verified by the fact that both the trace and Max-Eigen statistics exceeded their respective critical levels, exhibiting statistical significance. Table 3 Result of the unit root test At Level At First Difference LGDI LGDP LGDS d(LGDI) d(LGDP) d(LGDS) With Constant t-Statistic 1.9592 0.2357 3.544 -5.9991 -5.2447 -4.5746 Prob. 0.9998 0.9718 1 0 0.0001 0.0007 Constant & Trend t-Statistic 0.1589 -1.5845 -0.0587 -2.801 -5.4447 -6.5031 Prob. 0.9969 0.7823 0.994 0.2059 0.0003 0 Table 4 Lag order selection criteria Lag LogL LR FPE AIC SC HQ 0 152.7891 NA 1.35e-07 -7.306786 -7.181403 -7.261128 1 286.5067 241.3440* 3.08e-10* -13.39057* -12.88904* -13.20794* 2 291.2220 7.820457 3.82e-10 -13.18156 -12.30388 -12.86196 Table 5 Johansen’s cointegration Hypothesized Trace test Max-Eigen test No. of CE(s) statistics Critical Value Prob.** Statistic Critical Value Prob.** None 27.11886 29.79707 0.0988 16.91794 21.1316 0.1759 At most 1 10.20092 15.49471 0.2656 9.983096 14.2646 0.213 At most 2 0.217821 3.841465 0.6407 0.217821 3.84147 0.6407 4.5. Result of deployment of the Vector Autoregression Model (VAR) The findings obtained from the VAR model in Table 6 indicate how LGDI, LGDP, and LGDS expand logarithmically. The research demonstrates that a one-percentage point rise in previous LGDI leads to a 0.8457% increase in current LGDI, yet LGDP at the same time period causes LGDI to drop by 0.2613%. Previous savings levels create large unfavorable effects on current investment based on the LGDI model results. The results reveal that LGDP values increase by 0.8709% when their previous value increases by 1%. The model indicates that LGDS from the preceding era negatively affects LGDP. The value of LGDS in the prior period exhibited the strongest significant positive impact on LGDS, with a coefficient of 0.9805. The explanatory power of this model remains solid, with an R² value of 0.9624 for LGDI, 0.9571 for LGDP, and 0.9894 for LGDS. These data imply that most of the sample variances in these variables can be described by the model. The high F-statistics show that the model reaches overall statistical significance. The statistical significance test demonstrated that the LGDP variable had no impact on LGDI. The effectiveness of the model is supported by the Akaike and Schwarz criteria and validated by the low determinant values of residual covariance, which demonstrate its good fit to the data. Table 6 The deployment of the Vector Autoregression Model (VAR) LGDI LGDP LGDS LGDI(-1) 0.845693 -0.052766 0.002583 (0.11100) (0.06554) (0.00229) [ 7.61863] [-0.80508] [ 1.12590] LGDP(-1) -0.261333 0.870903 -0.010261 (0.23691) (0.13988) (0.00490) [-1.10311] [ 6.22608] [-2.09604] LGDS(-1) -9.977615 -3.399622 0.980467 (3.31159) (1.95530) (0.06843) [-3.01294] [-1.73867] [ 14.3282] C 252.1492 86.14757 0.490224 (83.7981) (49.4780) (1.73156) [ 3.00901] [ 1.74113] [ 0.28311] R-squared 0.962394 0.957128 0.989425 Adj. R-squared 0.959425 0.953743 0.988590 Sum sq. resids 0.496432 0.173067 0.000212 S.E. equation 0.114298 0.067486 0.002362 F-statistic 324.1566 282.7851 1185.122 Log likelihood 33.60213 55.73119 196.5363 Akaike AIC -1.409625 -2.463390 -9.168397 Schwarz SC -1.244133 -2.297898 -9.002905 Mean dependent 19.93142 5.727544 24.80802 S.D. dependent 0.567424 0.313782 0.022111 Determinant resid covariance (dof adj.) 2.30E-10 Determinant resid covariance 1.71E-10 Log likelihood 293.5313 Akaike information criterion -13.40625 Schwarz criterion -12.90978 Number of coefficients 12 4.6. Granger causality test Gross domestic investment (LGDI), gross domestic product (LGDP), and gross domestic savings (LGDS) exhibit predictive relationships based on the results of the Granger causality test in Table 7 . According to the test, past gross domestic saving data indicate statistically important information for anticipating present gross domestic investment (p-value = 0.0042). No Granger causality was found from LGDI to LGDS, as the p-value = 0.8390. The paired Granger causality test found no significant correlations between LGDI and LGDP or between LGDP and LGDS. Table 7 Granger causality test Null Hypothesis: Obs F-Statistic Prob. LGDP does not Granger Cause LGDI 42 1.17831 0.2844 LGDI does not Granger Cause LGDP 0.05494 0.8159 LGDS does not Granger Cause LGDI 42 9.23199 0.0042 LGDI does not Granger Cause LGDS 0.04185 0.8390 LGDS does not Granger Cause LGDP 42 2.45474 0.1252 LGDP does not Granger Cause LGDS 3.14960 0.0838 4.7. Impulse response function and variance decomposition The impulse response functions depicted in the Fig. 2 shows how a one-standard-deviation shock affects all variables (LGDI, LGDP, and LGDS) across the ten periods. The confidence intervals from the results enrich the study by indicating the statistical significance of the detected effects. The first row indicates a positive and statistically significant reaction of LGDI to its shock, which demonstrates that increased investment enhances future investment quantities. The results suggest that initial increases in LGDI in response to shocks in LGDP and LGDS lead to positive impacts that decline across the forecast range. The data in the second row show that LGDP reacts positively to its own perturbations at first, before the impacts decrease as time proceeds, because GDP growth patterns tend to normalize. Any temporary increase in investment results in GDP decline initially, which later normalizes and stabilizes economic activity. The final row reveals that LGDS reacts positively to its consequences, which indicates a persistent augmentation of saving levels following initial rises. LGDI and LGDP generate only minor but constructive influences on LGDS, which shows that investment, coupled with production variations, plays an enabling function in saving determination. The results in Table 8 illustrate the degree to which forecast mistakes in each variable are explained by internal and external shocks from other variables during a ten-period forecast period. The short-term changes in LGDI are attributable to its particular shocks, which decline progressively over time to enable LGDS to become the dominant driver of LGDI variations in Period 10 (41.87%). This study verifies the findings from Granger causality because savings prove to be more influential on investment as time proceeds. During the 10 periods, the volatility of LGDP originates largely from its shocks, despite other factors (LGDI and LGDS) maintaining an overall contribution of less than 27%. LGDS early impacts lead to 93.89% of its variability in period one, whereas LGDP increasingly becomes a greater predictor of LGDS variability, up to 61.01% by period ten. 4.8. Diagnostic test According in Table 9 shows a p-value of less than 5%; therefore, the null hypothesis is unproven. The null hypothesis states that serial correlation does not exist between the residuals from the VAR model. The analysis confirmed another successful validation of the model, because it showed no serial correlation. A test to validate the selected model should confirm the normal distribution of the residuals. Table 10 shows a test for assessing the residual variances. The probability derived by the Jarque-Bera test indicated values above 5%, thus confirming the inability to reject the null hypothesis. The null hypothesis evaluated the distribution of the residuals as normal. Table 8 Variance decomposition Variance Decomposition of LGDI: Period S.E. LGDI LGDP LGDS 1 0.114298 100.0000 0.000000 0.000000 2 0.149705 96.92448 0.749134 2.326387 3 0.174081 91.66180 1.426012 6.912183 4 0.194025 85.60055 1.582325 12.81713 5 0.211882 79.36726 1.373591 19.25915 6 0.228965 73.10816 1.268162 25.62367 7 0.246289 66.78632 1.806983 31.40670 8 0.264732 60.36780 3.431196 36.20100 9 0.285038 53.90218 6.370569 39.72726 10 0.307786 47.52508 10.60432 41.87060 Variance Decomposition of LGDP: Period S.E. LGDI LGDP LGDS 1 0.067486 26.05027 73.94973 0.000000 2 0.088949 23.41100 75.82397 0.765031 3 0.103847 20.72389 77.09942 2.176687 4 0.116363 18.16139 77.98265 3.855959 5 0.128003 15.81882 78.66891 5.512271 6 0.139433 13.73514 79.29883 6.966022 7 0.150967 11.91491 79.95330 8.131799 8 0.162757 10.34416 80.66606 8.989786 9 0.174864 9.000285 81.44039 9.559327 10 0.187298 7.857624 82.26320 9.879177 Variance Decomposition of LGDS: Period S.E. LGDI LGDP LGDS 1 0.002362 5.232231 0.878683 93.88909 2 0.003409 5.487179 6.104346 88.40847 3 0.004330 5.325577 13.59457 81.07986 4 0.005241 4.898856 21.95481 73.14633 5 0.006177 4.344312 30.25368 65.40201 6 0.007154 3.758472 37.98429 58.25724 7 0.008174 3.198674 44.93178 51.86955 8 0.009237 2.693787 51.05123 46.25499 9 0.010339 2.254812 56.38402 41.36117 10 0.011478 1.882646 61.00830 37.10905 Table 9 Serial correlation VAR Residual Serial Correlation LM Tests Lag LRE* stat df Prob. Rao F-stat df Prob. 1 7.777670 9 0.5567 0.867731 (9, 80.5) 0.5573 2 4.157421 9 0.9007 0.453823 (9, 80.5) 0.9009 Table 10 VAR residual normality test VAR Residual Normality Tests Component Skewness Chi-sq df Prob.* 1 -0.522308 1.909643 1 0.1670 2 -0.226053 0.357699 1 0.5498 3 -0.380778 1.014943 1 0.3137 Joint 3.282285 3 0.3501 Component Kurtosis Chi-sq df Prob. 1 4.053525 1.942353 1 0.1634 2 3.936380 1.534415 1 0.2155 3 4.400552 3.432705 1 0.0639 Joint 6.909473 3 0.0748 Component Jarque-Bera df Prob. 1 3.851996 2 0.1457 2 1.892113 2 0.3883 3 4.447649 2 0.1082 Joint 10.19176 6 0.1168 5. Conclusion and policy recommendations This study investigate the dynamic interaction between saving, investment, and economic growth in Somalia from 1989 to 2022 using a Vector Autoregressive (VAR) approach. The findings obtained from the VAR model indicate how LGDI, LGDP, and LGDS expand logarithmically. The research demonstrates that a one-percentage point rise in previous LGDI leads to a 0.8457% increase in current LGDI, yet LGDP at the same time period causes LGDI to drop by 0.2613%. Previous savings levels create large unfavorable effects on current investment based on the LGDI model results. The results reveal that LGDP values increase by 0.8709% when their previous value increases by 1%. The model indicates that LGDS from the preceding era negatively affects LGDP. The value of LGDS in the prior period exhibited the strongest significant positive impact on LGDS, with a coefficient of 0.9805. The explanatory power of this model remains solid, with an R² value of 0.9624 for LGDI, 0.9571 for LGDP, and 0.9894 for LGDS. These data imply that most of the sample variances in these variables can be described by the model. The high F-statistics show that the model reaches overall statistical significance. The statistical significance test demonstrated that the LGDP variable had no impact on LGDI. The effectiveness of the model is supported by the Akaike and Schwarz criteria and validated by the low determinant values of residual covariance, which demonstrate its good fit to the data. Furthermore, The Granger causality test found predictive relationships between gross domestic investment (LGDI), gross domestic product (LGDP), and gross domestic savings (LGDS). Past savings data are crucial for anticipating future investment, but no significant correlations were found. To foster sustainable economic growth in Somalia, several key policy recommendations should be considered. First, financial literacy programs should be implemented to educate the population, encouraging saving and informed investment decisions. Second, promoting political stability will build investor confidence, attracting both domestic and foreign investmens. Third, increase investments in education and healthcare is crucial for developing a skilled workforce, thereby supporting sustained economic growth. This study contributes empirical evidence on the complex relattionships among these crucial macroeconomic variables whith the Somalia economy. The study underscores the importance of addressing the unique challenges faced by Somalia, such as political instability and weak governance, which have hindered the country’s ability to achieve higher saving and investment levels and, consequently stronger economic growth. Moreover, future research should examining the dynamic interaction among saving, investment, and economic growth in Africa countries. Declarations Ethical approval This study follows all ethical practices during writing. We declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere. Consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding acquisition This research has no support. Author Contribution Abdishakur Mohamud Ahmed* Conceptualization, Methodology, Writing—Original Draft, Data Curation, Formal Analysis, Literature Review, Validation, Writing—Review & Editing. Data availability The datasets used and/or analyzed during the current study are available from the author on reasonable request. Please contact the corresponding author for data requests. References Solow RM. A contribution to the theory of economic growth. Q J Econ. 1956;70(1):65–94. 10.2307/1884513 . Romer PM. Increasing Returns and Long-Run Growth. J Polit Econ. 1986;94(5):1002–37. 10.1086/261420 . Taylor JB. Discretion versus policy rules in practice. Carnegie-Rochester Conf Ser Public Policy. Dec. 1993;39:195–214. 10.1016/0167-2231(93)90009-L . Fischer. Fischer_Macro_Factors. 1993. Fund IM, Outlook WE. 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Shahbaz M. Old Wine in New Bottles : Saving – Growth Nexus : Innovative Accounting Technique in Pakistan, vol. XVII, no. 7, pp. 49–60, 2010. Misztal P, THE RELATIONSHIP BETWEEN SAVINGS AND ECONOMIC GROWTH IN COUNTRIES WITH DIFFERENT LEVEL OF ECONOMIC DEVELOPMENT. The relationship between savings and economic growth in countries with different level of economic development, 2011, [Online]. Available: https://hdl.handle.net/10419/66731 Sothan S. Causal Relationship between Domestic Saving and Economic Growth: Evidence from Cambodia. Int J Econ Financ. 2014;6(9):213–20. 10.5539/ijef.v6n9p213 . van Wyk BF, Kapingura FM. Understanding the nexus between savings and economic growth: A South African context. Dev South Afr. 2021;38(5):828–44. 10.1080/0376835X.2021.1932424 . Morina F, Misiri V, Gashi F. Long-term relationship between investment and economic growth: a cointegration analysis of OECD countries. Eur J Gov Econ. 2023;12(2):175–95. 10.17979/ejge.2023.12.2.9909 . Ben Yedder N, Weriemmi ME, Bakari S. The Impact of Domestic Investment and Trade on Economic Growth in North Africa Countries: New Evidence from Panel CS-ARDL Model, Munich Pers. RePEc Arch. , vol. 2, no. 79436, pp. 0–17, 2023, [Online]. Available: https://mpra.ub.uni-muenchen.de/79436/ Juliansyah H, Suprianti S, Nailufar F. Influence of Saving and Economic Growth on Investment in Indonesia the Period of 1990–2019. J Malikussaleh Public Econ. 2021;4(2). 10.29103/jmpe.v4i2.6039 . Maune A, Matanda E. The Nexus between Gross Capital Formation and Economic Growth: Evidence from Zimbabwe. J Acc Manag. 2022;12(2):33–44. Alzghoul A, Al Kasasbeh O, Alsheikh G, Yamin I. the Relationship Between Savings and Investment: Evidence From Jordan. Int J Prof Bus Rev. 2023;8(3):1–14. 10.26668/businessreview/2023.v8i3.1724 . Al-Afeef M, Al-Qudah A. The casual relationship between savings and investment in Jordan, a prospective study for the period 1980–2013, J. Econ. Sustain. Dev. , vol. 6, no. 10, pp. 229–238, 2015, [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167320253&partnerID=40&md5=8d49c9921055c259985d988bdeb0ccfd Jangili R. Munich Personal RePEc Archive Causal relationship between saving, investment and economic growth for India – what does the relation imply ? Causal Relationship between Saving, Investment and Economic Growth for India, no. 40002, 2012. Lira PS, Kalebe MK. Savings, investment and economic growth in Lesotho: An empirical analysis. J Econ Int Financ. 2015;7(10):213–21. 10.5897/jeif2015.0708 . Dritsaki C. The Long-Run Relationship between Saving and Investment in Greece. Int J Econ Financ. 2015;7(9):178–92. 10.5539/ijef.v7n9p178 . Ayetuoma Ogbokor C, Andiya Musilika O. Investigating the Relationship between Aggregate Savings and Investment in Namibia: A Causality Analysis. Res J Financ Acc www iiste org ISSN. 2014;5(6):82–9. [Online]. Available: www.iiste.org. Ramakrishna G, Rao SV. The Long run Relationship between Savings and Investment in Ethiopia : a Cointegration and ECM Approach, 2, 4, pp. 1–8, 2012. Ngouhouo I, Mouchili E. Saving, Investment and Economic Growth in Cameroun: A Multivariate Approach. Int J Econ Financ. 2014;6(9):244–52. 10.5539/ijef.v6n9p244 . Sy WN. Saving and Investment in US Economic Growth. SSRN Electron J. 2014;1–22. 10.2139/ssrn.2529559 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6434327","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458833929,"identity":"64aa8efb-8688-41ba-acca-71a3a4a4b095","order_by":0,"name":"Abdishakur Mohamud Ahmed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYDACCQaDA4wNDAYg9oEEAwk5MOMBkVoYD3yosDCG6CWghQGqhfngjDMViQ0gUXxa+Gc3bzz4c4edscGN3AeHedsk0ueHHX4ItMVOTrcBhyV3jhUc5j2TbGZwI90ApCV34+00A6CWZGOzAzisuZFjcJixjdnG4EYaA0TL7ASQlgOJ23BokQdqOfizrR6uJd1wdvoHvFoMgFoO8LYdNgNpAXpfIkFeOge/LYZgv7QdN5Y884wBGMgShhukcwqAEYTbL3K3mzd//NlWbdh3PI35Q4JBnbz87PTNHz5U2Mnh9D6mU8EqDYhVDgLyDaSoHgWjYBSMgpEAAKX9bTwbEJL0AAAAAElFTkSuQmCC","orcid":"","institution":"SIMAD University","correspondingAuthor":true,"prefix":"","firstName":"Abdishakur","middleName":"Mohamud","lastName":"Ahmed","suffix":""}],"badges":[],"createdAt":"2025-04-12 11:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6434327/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6434327/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83196360,"identity":"d55d9b52-28a2-4428-baee-d052d301bbea","added_by":"auto","created_at":"2025-05-21 05:36:07","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":141298,"visible":true,"origin":"","legend":"\u003cp\u003eTrend analysis of variables\u003c/p\u003e","description":"","filename":"floatimage117.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6434327/v1/7aa341a18f8d240025bd6423.jpeg"},{"id":83196356,"identity":"e23acc7a-14f6-422e-b6e9-ef4d5742ab03","added_by":"auto","created_at":"2025-05-21 05:36:07","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":579667,"visible":true,"origin":"","legend":"\u003cp\u003eImpulse response function\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6434327/v1/63bb7712a3c978c075ec3fbf.jpeg"},{"id":84460153,"identity":"f52c5156-c97f-4c1b-8eb4-caa6dacfbf53","added_by":"auto","created_at":"2025-06-12 08:39:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1831569,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6434327/v1/26fc00b6-7ee4-418a-bec4-20621a98ff33.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDynamic Interaction Between Savings, Investment and Economic Growth in Somalia: A Vector Autoregressive (Var) Approach\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEconomic stability, capital accumulation, and long-term development depend on the dynamic interplay between savings, investment, and global economic growth [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Investment necessitates accumulated capital, and this process fosters economic progress by enhancing production performance and expansion [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], the economic environment stabilizes more effectively when inflation is low and interest rates are stable, as these conditions promote increased savings and hence enhance investments that drive growth. However, the relationship between saving, investment, and economic growth encounters several challenges, as fluctuating interest rates hinder borrowers and investors, whereas rampant inflation diminishes the value of savings [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. External economic downturns diminish investor confidence, resulting in disrupted capital flows [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The global correlation between savings, economic growth, and investment is complicated by external economic disruptions such as the COVID-19 pandemic and escalating geopolitical tensions. During the pandemic, worldwide savings rates significantly declined as people relied on their savings to endure income decreases; concurrently, developing countries experienced a 20% decrease in investment capital from 2020 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Global investment growth, measured as annual growth in real gross fixed capital formation, has decreased from 3.3% in 2022 to only 1.9% in 2023. While mild increase for 2024; nonetheless, investment rates will remain below the trend rates of 2011\u0026ndash;2019 at 4% [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOver the past two decades, economic advancements in sub-Saharan Africa have stemmed from substantial expenditures in infrastructure and natural resources, which are uneven, particularly among oil-exporting countries, as noted by the [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The countries hardest hit by fluctuations in oil prices, especially Angola, Nigeria, and the CEMAC member states, have to struggle with significant declines in budget revenues and balance-of-payments problems [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. According to the [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], two African countries, notably Botswana and Ghana, demonstrated substantial savings rate gains that fostered investment and economic expansion. This area faces hurdles that dissuade investors both at home and abroad, thereby decreasing its capacity to achieve sustainable economic development. Unpredictable power supply, along with poor transportation networks, elevates corporate operating expenditures, which decreases investment opportunities for FDI in the area [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, Sub-Saharan African economies have historically low savings rates, averaging around 17% of GDP, whereas the world standard is 25%. Low savings levels hinder local investment options because many countries must accept external financial assistance that includes severe requirements and boosts debt levels [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSomalia needs well-established links between savings and investment to achieve economic growth, which leads to greater economic development. The country suffers considerable challenges, such as civil war coupled with political instability and weak governance, which impede both investment and economic expansion [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Somalia's financial system has become more robust in recent years, while attracting investments that mostly focus on the telecom and construction fields [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The economic prosperity of Somalia hinges mainly on investments made in education and healthcare. The school system has suffered greatly because of the civil war; however, education expenditure remains crucial for generating skilled individuals [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The real GDP growth rate rose from 2.4 percent in 2022 to 3.3 percent in 2021 as Somalia began overcoming the COVID-19 pandemic effects that caused negative growth in 2020 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. According to [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], the nation's investment levels remain sluggish because the average gross capital creation reaches just 13\u0026ndash;14% of GDP from 2012 to 2020.\u003c/p\u003e \u003cp\u003eThe economic status of Somalia has been damaged by continuous warfare paired with political instability and low institutional capabilities, so it remains unable to develop savings while attracting investment. Somalia maintains one of the world's lowest savings rates because poverty, along with insufficient banking institutions and informal economic structures, affects more than 5% of the GDP [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The scarcity of domestic savings has hindered Somalia from developing its capital accumulation, which has led the nation to depend largely on foreign aid and remittances to fund its basic requirements and reconstruction endeavors. More than 30 percent of the national GDP comes from remittances, yet these funds mostly support household expenditure, which precludes sustained development [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Somalia presents hurdles to investment because of security threats, poor governance systems, and limited infrastructure that make both indigenous and foreign investors apprehensive. Insufficient regulatory stability, along with inactive financial institutions, provides considerable investment obstacles to enterprises working in the agricultural, industrial, and energy sectors. The absence of credit and collateral infrastructure prohibits firms from establishing or expanding; thus, they cannot create jobs while fighting poverty [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough the country has various hurdles, Somalia has multiple promising economic growth opportunities within its cattle and fisheries sectors, alongside renewable energy development. Somalia aims to gain from its geographic position between the Gulf of Aden and the Indian Ocean by offering new commercial opportunities for port development and logistical infrastructure. The World Bank support the possibility of improving economic stability owing to state-building progress and federal government development [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This study seeks to address this gap by examining the dynamic interactions among investment, savings, and economic growth in Somalia from 1989 to 2022. This study utilizes the vector autoregressive technique (VAR), which is most suitable because of the behavior of the variables involved. This study employs several econometric methods to ensure robust and reliable results. These include unit root tests, Johansen cointegration method, limits testing, Granger causality analysis, impulse response functions, and variance decomposition analysis. Additionally, the study provides data that might lead to specific strategies for solving the difficulties faced by barriers to investment, elevated unemployment, and overall economic stability in Somalia.\u003c/p\u003e \u003cp\u003eThe subsequent sections of the study are structured as follows: The second portion covers the empirical literature, and section presents the theoretical framework, data sources, model definition, and econometric technique. Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the empirical analysis and debates, and the concluding section summarizes the study and provides policy recommendations.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eIn recent decades, literature has consistently examined the relationship between savings, investment, and economic growth. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] analyzed the dynamic interplay among saving, investment, and economic growth in Ethiopia from 1992/1993 to 2019/20 utilizing quarterly data. The findings from the Vector Error Correction Model (VECM) demonstrate a positive long-term link among savings, investment, and economic growth. The Granger causality test indicates that domestic investment and real GDP cause domestic savings, whereas domestic investment causes economic growth. These results underscore the significance of business savings in stimulating economic growth. Additionally, [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] investigated the correlation between domestic savings and sustainable economic growth in South Africa from 1990 to 2023 utilizing the ARDL framework. The results demonstrate that corporate savings significantly impact sustainable economic growth, suggesting that effective mobilization of corporate savings is essential for promoting economic stability and growth. Numerous studies have identified a positive correlation between savings, investment, and economic growth [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]; [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]; [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. While data from Nigeria indicated considerable connectivity between the three variables, it also indicated that GDP had a larger influence on both gross national saving and gross capital formation, [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Furthermore, studies in Nepal found a positive association between gross domestic product and gross domestic savings in the long run but a negative correlation in the short run. In addition, gross domestic product maintained a positive link with gross capital formation in both the long and short term [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies have found a complex relationship between savings and economic growth. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] studied the causal relationship between gross domestic saving and economic growth in six sub-Saharan nations from 1981 to 2014 using the ARDL and Toda-Yamamoto techniques. They observed unidirectional causality economic growth to gross domestic saving in Ghan and Burkina-Faso, but gross domestic saving Granger causes in economic growth in Liberia, Niger, and Sierra Leone. No causal links have been found in Nigeria. In contrast, [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] focus on Nigeria from 1981 to 2014, utilizing the Vector Autoregressive (VAR) approach. Their funding revealed a unidirectional causal association between gross domestic product (GDP) and gross domestic savings (GDS), indicating that economic expansion affects savings. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] studied the relationship between economic development and domestic saving in Pakistan from 1971 to 2007 using ARDL and Granger causality analysis. They observed a long-run link between economic growth and domestic savings, demonstrating that economic expansion leads to more savings, whereas there is no reciprocal causality from saving to growth.\u003c/p\u003e \u003cp\u003eIn addition, we analyze the causal association between savings and economic development in both advanced and emerging economies from 1980 to 2010. They observed a unidirectional causal relationship, in which economic expansion stimulates savings in both types of economies, but not vice versa [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Similarly, studies in Nepal from 1974/75 to 2018/19 using the ARDL technique demonstrated unidirectional causality from economic growth to saving, showing that economic expansion stimulates saving but not vice versa. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] studied the causal relationship between household saving and economic growth in Cambodia from 1989 to 2012 using the Granger causality test. They observed no causal association between domestic saving and economic growth; specifically, domestic saving does not Granger-cause economic growth, and economic growth does not Granger-cause savings. Using the Vector Error Correction Model (VECM), [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] evaluated time-series data from 1986 to 2018 and studied the link between saving and economic growth. Furthermore, they observe a negative correlation between savings and economic growth.\u003c/p\u003e \u003cp\u003eLiterature suggests that the degree of investment plays a significant role in driving economic growth. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] evaluated the long-term association between investment and economic growth in OECD countries. Their investigation found a strong correlation between domestic investments and economic growth. Using the panel CS-ARDL model in North African countries, [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] found a favorable impact of domestic investment on economic growth in the long and short run. In addition, [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], using the Vector Error Correction Model (VECM), showed that economic growth has no influence on investment in the short run, but long-run economic growth has a positive impact on investment. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] studied the link between gross capital formation and economic growth in Zimbabwe, using the autoregressive approach (ARDL). They discovered that gross capital formation was good, but not considerable, economic growth.\u003c/p\u003e \u003cp\u003eHowever, the literature reveals that saving has a complex relationship with economic growth. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] studied the relationship between saving and investment evidence from Jordan, using the autoregressive approach (ARDL). They observe a positive connection between savings and investment. This study reveals essential domestic savings in boosting investment and growth [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]; [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]; and [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. They observe unidirectional causality from savings to investment. Similarly, both studies [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], they find unidirectional causation from saving to investment, demonstrating a lack of long-run association between the two variables, which implies considerable capital mobility. On the other hand, [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] studied the long-run link between saving and investment in Ethiopia. They discover no causation between savings and investment in either way. In addition, [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] they identified a unidirectional causal relationship from investment to saving and economic growth. This study criticizes the common macroeconomic assumption that \u0026ldquo;saving equal\u0026rsquo;s investment,\u0026rdquo; suggesting that this approach leads to serious errors in economic analysis [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. They concluded that the U.S. economy has had a negative saving rate for over three decades, which has resulted in a stagnating economy defined by poor productivity and high debt levels.\u003c/p\u003e \u003cp\u003eIn summary, the literature illustrates the complex link between savings, investment, and economic growth. This highlights the significance of considering multiple econometric techniques to understand the dynamics of these relationships. Despite numerous empirical studies on savings, investment, and economic growth in recent years, the literature has generally disregarded the interconnectedness of these variables. This study analyzes the causal linkages between savings, investment, and economic growth. Unlike prior studies, our research focuses on the specific economic context of Somalia by applying a comprehensive set of econometric approaches to discover the complicated association between these crucial variables.\u003c/p\u003e"},{"header":"3. Methodology and Data","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Sampling and data source\u003c/h2\u003e \u003cp\u003eData from 1980 to 2022 has been used to develop a time series model for the study. The data was acquired from the Organization of Islamic Cooperation (OIC-SESRIC). The variables used in this study include gross domestic output, gross domestic investment, and gross domestic saving, as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables and data source\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProxy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeasurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDP per Capita (Constant 2015 Prices)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSesric\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross domestic investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGross Capital Formation, Constant 2015 Prices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSesric\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross domestic saving\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGross Domestic Savings, Constant 2015 Prices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSesric\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe trend analysis of Somalia's economic variables\u0026mdash;Gross Domestic Product (GDP), Gross Domestic Investment (GDI), and Gross Domestic Saving (GDS)\u0026mdash;from 1989 to 2022 indicates important patterns across the years.\u003c/p\u003e \u003cp\u003eStarting in 1989, the GDP was reported at around 5.56 and exhibited a steady climb in the next years to reach roughly 5.89 by 2011. This upward trend shows a period of economic improvement, despite minor fluctuations throughout the early 1990s, particularly during the civil instability in 1991\u0026ndash;1992, where GDP experienced slight drops but steadied subsequently. Especially, the GDP peaked in 2019 at 6.34 before considerably dropping in 2020 and 2021, showing some economic problems in recent times.\u003c/p\u003e \u003cp\u003eOn the contrary, hand, GDI showed a more continuous upward trend across the years, commencing at 19.85 in 1989 and slowly rising to 21.49 by 2022. This demonstrates a constant economic investment, which is extremely crucial for encouraging development and advancement. The GDI's durability implies that there has been a dedication to improve investment levels despite economic constraints.\u003c/p\u003e \u003cp\u003eFinally, GDS demonstrated more flux. Beginning at 4.05 in 1989, it kept reasonably constant until 2012, when it clearly dropped to -0.92, signaling a time of dissaving. This was followed by a recovery phase, with GDS growing to 2.64 in 2021 before decreasing slightly to 2.37 in 2022. The swings in savings could reflect variations in economic stability and household confidence, highlighting the necessity of policies that encourage saving behavior.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Unit root test\u003c/h2\u003e \u003cp\u003eAs the model applied in this study involves a temporal trend (t), nonstationarity suggests that a normal regression analysis will yield misleading and untrustworthy results. Therefore, it is necessary to check whether the time-series variables are integrated of order one before executing the VAR test. To do this, the augmented Dickey (ADF) unit root test was applied. The alternative hypothesis (H1) contends that the series is stationary, whereas the null hypothesis (H0) asserts that the series has a unit root.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Vector autoregressive approach\u003c/h2\u003e \u003cp\u003eThe VAR models adopted to examine the dynamic interaction among the variables used in this study are expressed as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{LGDI}_{t}={\\propto\\:}_{1}+\\sum\\:_{j=1}^{n}{\\beta\\:}_{j}{LGDI}_{t-j}+\\sum\\:_{j=1}^{n}{\\theta\\:}_{j}{LGDP}_{t-j}+\\sum\\:_{j=1}^{n}{\\gamma\\:}_{j}{LGDS}_{t-j}+{\\mu\\:}_{1t}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{LGDP}_{t}={\\propto\\:}_{2}+\\sum\\:_{j=1}^{n}{\\theta\\:}_{j}{LGDP}_{t-j}+\\sum\\:_{j=1}^{n}{\\beta\\:}_{j}{LGDI}_{t-j}+\\sum\\:_{j=1}^{n}{\\gamma\\:}_{j}{LGDS}_{t-j}+{\\mu\\:}_{2t}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{LGDS}_{t}={\\propto\\:}_{3}+\\sum\\:_{j=1}^{n}{\\gamma\\:}_{j}{LGDS}_{t-j}+\\sum\\:_{j=1}^{n}{\\beta\\:}_{j}{LGDP}_{t-j}+\\sum\\:_{j=1}^{n}{\\theta\\:}_{j}{LGDI}_{t-j}+{\\mu\\:}_{3t}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe parameters are denoted by the coefficients β, θ, and γ in the VAR equation above, where n denotes the lag order. L represents logarithm, \u0026micro; is the stochastic error term termed impulses, innovations, or shocks in VAR, and t is the current time. The first stage in estimating the VAR model is the application of a root test that validates the condition of stationarity for each chosen endogenous variable, the second step lag order selection, the third step Johansen cointegration test, and the fourth step estimation of VAR.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Result and discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Descriptive statistics\u003c/h2\u003e \u003cp\u003eThe preliminary summary statistics and correlation matrix for the major factors describing the raw data are provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e using Eviews 12. According to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the mean for gross domestic savings (GDS) is the highest at 24.81, whereas the mean for Gross Domestic Product is the lowest at 5.72. Notably, GDS has its highest maximum value of 24.83, whereas Gross Domestic Product has the lowest minimum of 5.38. One notable result is that the Gross Domestic Investment (GDI) has the greatest standard deviation (0.56), indicating a greater range of values around its mean. The Jarque-Bera test results showed that the data were identically and normally distributed. Panel B presents the correlation matrix of the variables. Except for gross domestic savings, all the variables suggest a positive connection with GDI. There is a negative correlation between gross domestic saving and GDI. Similarly, all parameters, excluding unemployment, suggest a positive connection with GDI. A positive correlation exists between the gross domestic product (GDP) and gross domestic investment (GDI).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePanel A: Descriptive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eLGDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eLGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eLGDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e19.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e24.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e19.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e5.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e24.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e21.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e24.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e19.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e24.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJarque-Bera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e12.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e6.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e8.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProbability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePanel B: Correlation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eLGDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eLGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eLGDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLGDI\u003c/p\u003e \u003cp\u003eLGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLGDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Lag length criteria\u003c/h2\u003e \u003cp\u003eThis study employs the majority technique to determine the ideal lag length. The five criteria studied were the sequential modified LR test statistic (LR), final prediction error (FPE), Akaike information criterion (AIC), Schwarz information criterion (SIC), and Hannan-Quinn information criterion (HQ), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. All five considerations show that the optimal lag length is one.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Unit root test\u003c/h2\u003e \u003cp\u003eThis study adopts a model with a temporal trend (t) for which non-stationarity could lead to erroneous conclusions from a simple regression analysis. It is necessary to check whether the time-series variables reflect first-order integration before executing the vector autoregressive (VAR) model. To this end, this study employs the ADF unit root test. In the ADF test, the alternative hypothesis implies the absence of a unit root problem, whereas the null hypothesis suggests the existence of one. Therefore, if the variable\u0026rsquo;s t-statistic is larger than the essential t-value, we agree that the data are stationary and reject the null hypothesis of nonstationarity. Conversely, if the t-statistic is less than the critical t-value, the nonstationarity null hypothesis cannot be ruled out. Essentially, these tests assist in identifying whether the data exhibit constant mean and variance over time. Moreover, all variables are nonstationary, as demonstrated by the results of the ADF unit root test shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Using nonstationary variables can lead to erroneous results with serious repercussions. The results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrate that all the variables are stationary at the initial difference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Cointegration test\u003c/h2\u003e \u003cp\u003eThe long-term association between time-series variables can be statistically established by applying the Johansen cointegration max-eigenvalue and trace tests. These tests consider the null hypothesis that there are no co-integrating vectors, and calculate the rank of the co-integration matrix. The Johansen Max-Eigenvalue and trace test results, presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, provide evidence of no co-integrating equations among the variables in Somalia. This result was verified by the fact that both the trace and Max-Eigen statistics exceeded their respective critical levels, exhibiting statistical significance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResult of the unit root test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAt Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eAt First Difference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLGDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLGDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ed(LGDI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ed(LGDP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ed(LGDS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith Constant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003et-Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.9991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-5.2447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-4.5746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant \u0026amp; Trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003et-Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.5845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-5.4447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-6.5031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLag order selection criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLag\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFPE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHQ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152.7891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35e-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.306786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-7.181403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-7.261128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e286.5067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e241.3440*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.08e-10*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13.39057*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-12.88904*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-13.20794*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e291.2220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.820457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.82e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13.18156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-12.30388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-12.86196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eJohansen\u0026rsquo;s cointegration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesized\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eTrace test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eMax-Eigen test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of CE(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003estatistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCritical Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProb.**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCritical Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eProb.**\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.11886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.79707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.91794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21.1316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1759\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt most 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.20092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.49471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.983096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.2646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt most 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.217821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.841465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.217821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.84147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6407\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Result of deployment of the Vector Autoregression Model (VAR)\u003c/h2\u003e \u003cp\u003eThe findings obtained from the VAR model in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e indicate how LGDI, LGDP, and LGDS expand logarithmically. The research demonstrates that a one-percentage point rise in previous LGDI leads to a 0.8457% increase in current LGDI, yet LGDP at the same time period causes LGDI to drop by 0.2613%. Previous savings levels create large unfavorable effects on current investment based on the LGDI model results. The results reveal that LGDP values increase by 0.8709% when their previous value increases by 1%. The model indicates that LGDS from the preceding era negatively affects LGDP. The value of LGDS in the prior period exhibited the strongest significant positive impact on LGDS, with a coefficient of 0.9805. The explanatory power of this model remains solid, with an R\u0026sup2; value of 0.9624 for LGDI, 0.9571 for LGDP, and 0.9894 for LGDS. These data imply that most of the sample variances in these variables can be described by the model. The high F-statistics show that the model reaches overall statistical significance. The statistical significance test demonstrated that the LGDP variable had no impact on LGDI. The effectiveness of the model is supported by the Akaike and Schwarz criteria and validated by the low determinant values of residual covariance, which demonstrate its good fit to the data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe deployment of the Vector Autoregression Model (VAR)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLGDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLGDP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLGDS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDI(-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.845693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.052766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.11100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.06554)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.00229)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[ 7.61863]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-0.80508]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[ 1.12590]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDP(-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.261333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.870903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.010261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.23691)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.13988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.00490)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-1.10311]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[ 6.22608]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[-2.09604]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDS(-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-9.977615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.399622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.980467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.31159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.95530)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.06843)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-3.01294]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-1.73867]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[ 14.3282]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e252.1492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.14757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.490224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(83.7981)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(49.4780)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.73156)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[ 3.00901]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[ 1.74113]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[ 0.28311]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.962394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.957128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.989425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.959425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.953743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.988590\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSum sq. resids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.496432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.173067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.E. equation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.114298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.067486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e324.1566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e282.7851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1185.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.60213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.73119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e196.5363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAkaike AIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.409625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.463390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.168397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchwarz SC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.244133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.297898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.002905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean dependent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.93142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.727544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.80802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.D. dependent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.567424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.313782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDeterminant resid covariance (dof adj.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.30E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDeterminant resid covariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.71E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLog likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e293.5313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAkaike information criterion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-13.40625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSchwarz criterion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-12.90978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNumber of coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Granger causality test\u003c/h2\u003e \u003cp\u003eGross domestic investment (LGDI), gross domestic product (LGDP), and gross domestic savings (LGDS) exhibit predictive relationships based on the results of the Granger causality test in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. According to the test, past gross domestic saving data indicate statistically important information for anticipating present gross domestic investment (p-value\u0026thinsp;=\u0026thinsp;0.0042). No Granger causality was found from LGDI to LGDS, as the p-value\u0026thinsp;=\u0026thinsp;0.8390. The paired Granger causality test found no significant correlations between LGDI and LGDP or between LGDP and LGDS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGranger causality test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNull Hypothesis:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDP does not Granger Cause LGDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLGDI does not Granger Cause LGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDS does not Granger Cause LGDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.23199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLGDI does not Granger Cause LGDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8390\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDS does not Granger Cause LGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.45474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLGDP does not Granger Cause LGDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.14960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.7. Impulse response function and variance decomposition\u003c/h2\u003e \u003cp\u003eThe impulse response functions depicted in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows how a one-standard-deviation shock affects all variables (LGDI, LGDP, and LGDS) across the ten periods. The confidence intervals from the results enrich the study by indicating the statistical significance of the detected effects.\u003c/p\u003e \u003cp\u003eThe first row indicates a positive and statistically significant reaction of LGDI to its shock, which demonstrates that increased investment enhances future investment quantities. The results suggest that initial increases in LGDI in response to shocks in LGDP and LGDS lead to positive impacts that decline across the forecast range. The data in the second row show that LGDP reacts positively to its own perturbations at first, before the impacts decrease as time proceeds, because GDP growth patterns tend to normalize. Any temporary increase in investment results in GDP decline initially, which later normalizes and stabilizes economic activity. The final row reveals that LGDS reacts positively to its consequences, which indicates a persistent augmentation of saving levels following initial rises. LGDI and LGDP generate only minor but constructive influences on LGDS, which shows that investment, coupled with production variations, plays an enabling function in saving determination.\u003c/p\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e illustrate the degree to which forecast mistakes in each variable are explained by internal and external shocks from other variables during a ten-period forecast period. The short-term changes in LGDI are attributable to its particular shocks, which decline progressively over time to enable LGDS to become the dominant driver of LGDI variations in Period 10 (41.87%). This study verifies the findings from Granger causality because savings prove to be more influential on investment as time proceeds. During the 10 periods, the volatility of LGDP originates largely from its shocks, despite other factors (LGDI and LGDS) maintaining an overall contribution of less than 27%. LGDS early impacts lead to 93.89% of its variability in period one, whereas LGDP increasingly becomes a greater predictor of LGDS variability, up to 61.01% by period ten.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.8. Diagnostic test\u003c/h2\u003e \u003cp\u003eAccording in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows a p-value of less than 5%; therefore, the null hypothesis is unproven. The null hypothesis states that serial correlation does not exist between the residuals from the VAR model. The analysis confirmed another successful validation of the model, because it showed no serial correlation. A test to validate the selected model should confirm the normal distribution of the residuals. Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows a test for assessing the residual variances. The probability derived by the Jarque-Bera test indicated values above 5%, thus confirming the inability to reject the null hypothesis. The null hypothesis evaluated the distribution of the residuals as normal.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariance decomposition\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance Decomposition of LGDI:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLGDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLGDS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.114298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.149705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.92448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.749134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.326387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.174081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.66180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.426012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.912183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.194025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.60055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.582325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.81713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.211882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.36726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.373591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.25915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.228965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.10816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.268162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.62367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.246289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.78632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.806983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.40670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.264732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.36780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.431196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.20100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.285038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.90218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.370569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.72726\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.307786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.52508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.60432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.87060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance Decomposition of LGDP:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLGDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLGDS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.067486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.05027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.94973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.088949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.41100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.82397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.765031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.103847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.72389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.09942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.176687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.116363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.16139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.98265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.855959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.128003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.81882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.66891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.512271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.139433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.73514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.29883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.966022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.150967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.91491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.95330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.131799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.162757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.34416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.66606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.989786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.174864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.000285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.44039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.559327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.187298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.857624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.26320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.879177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance Decomposition of LGDS:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLGDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLGDS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.232231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.878683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93.88909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.487179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.104346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.40847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.325577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.59457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.07986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.898856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.95481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.14633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.344312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.25368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.40201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.758472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.98429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.25724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.198674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.93178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.86955\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.009237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.693787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.05123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.25499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.010339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.254812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.38402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.36117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.011478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.882646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.00830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.10905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSerial correlation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eVAR Residual Serial Correlation LM Tests\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLRE* stat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRao F-stat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.777670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.867731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(9, 80.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5573\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.157421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.453823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(9, 80.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVAR residual normality test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eVAR Residual Normality Tests\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChi-sq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProb.*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.522308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.909643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.226053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.357699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.380778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.014943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eJoint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.282285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChi-sq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.053525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.942353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1634\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.936380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.534415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.400552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.432705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0639\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eJoint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.909473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJarque-Bera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.851996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.892113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.447649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eJoint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.19176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion and policy recommendations","content":"\u003cp\u003eThis study investigate the dynamic interaction between saving, investment, and economic growth in Somalia from 1989 to 2022 using a Vector Autoregressive (VAR) approach. The findings obtained from the VAR model indicate how LGDI, LGDP, and LGDS expand logarithmically. The research demonstrates that a one-percentage point rise in previous LGDI leads to a 0.8457% increase in current LGDI, yet LGDP at the same time period causes LGDI to drop by 0.2613%. Previous savings levels create large unfavorable effects on current investment based on the LGDI model results. The results reveal that LGDP values increase by 0.8709% when their previous value increases by 1%. The model indicates that LGDS from the preceding era negatively affects LGDP. The value of LGDS in the prior period exhibited the strongest significant positive impact on LGDS, with a coefficient of 0.9805. The explanatory power of this model remains solid, with an R\u0026sup2; value of 0.9624 for LGDI, 0.9571 for LGDP, and 0.9894 for LGDS. These data imply that most of the sample variances in these variables can be described by the model. The high F-statistics show that the model reaches overall statistical significance. The statistical significance test demonstrated that the LGDP variable had no impact on LGDI. The effectiveness of the model is supported by the Akaike and Schwarz criteria and validated by the low determinant values of residual covariance, which demonstrate its good fit to the data. Furthermore, The Granger causality test found predictive relationships between gross domestic investment (LGDI), gross domestic product (LGDP), and gross domestic savings (LGDS). Past savings data are crucial for anticipating future investment, but no significant correlations were found. To foster sustainable economic growth in Somalia, several key policy recommendations should be considered. First, financial literacy programs should be implemented to educate the population, encouraging saving and informed investment decisions. Second, promoting political stability will build investor confidence, attracting both domestic and foreign investmens. Third, increase investments in education and healthcare is crucial for developing a skilled workforce, thereby supporting sustained economic growth. This study contributes empirical evidence on the complex relattionships among these crucial macroeconomic variables whith the Somalia economy. The study underscores the importance of addressing the unique challenges faced by Somalia, such as political instability and weak governance, which have hindered the country\u0026rsquo;s ability to achieve higher saving and investment levels and, consequently stronger economic growth. Moreover, future research should examining the dynamic interaction among saving, investment, and economic growth in Africa countries.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthical approval\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study follows all ethical practices during writing. We declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent to participate\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding acquisition\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research has no support.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAbdishakur Mohamud Ahmed* Conceptualization, Methodology, Writing\u0026mdash;Original Draft, Data Curation, Formal Analysis, Literature Review, Validation, Writing\u0026mdash;Review \u0026amp; Editing.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets used and/or analyzed during the current study are available from the author on reasonable request. 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SSRN Electron J. 2014;1\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2139/ssrn.2529559\u003c/span\u003e\u003cspan address=\"10.2139/ssrn.2529559\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Saving, Investment, Economic growth, Vector autoregressive (VAR) approach","lastPublishedDoi":"10.21203/rs.3.rs-6434327/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6434327/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigate the dynamic interaction between saving, investment, and economic growth in Somalia from 1989 to 2022 using a Vector Autoregressive (VAR) approach. The findings indicate that a one-percentage point increase in previous LGDI leads to a 0.8457% increase in current LGDI, while LGDP causes a drop in LGDI. Previous savings levels have unfavorable effects on current investment, with LGDP values increasing by 0.8709% when their previous value increases by 1%. The model's explanatory power is solid, with high F-statistics and statistical significance. The model's effectiveness is supported by Akaike and Schwarz criteria and low residual covariance values. While Granger causality test found predictive relationships between gross domestic investment (LGDI), gross domestic product (LGDP), and gross domestic savings (LGDS). Past savings data are crucial for anticipating future investment, but no significant correlations were found. Based on the empirical evidence, the study provides several policy impilications, including financial literacy programs, promoting political stability, and increase investments in education and healthcare.\u003c/p\u003e","manuscriptTitle":"Dynamic Interaction Between Savings, Investment and Economic Growth in Somalia: A Vector Autoregressive (Var) Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-21 05:35:59","doi":"10.21203/rs.3.rs-6434327/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9bd9ed57-00b1-4a03-8c72-770f5dbc79a7","owner":[],"postedDate":"May 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-12T08:38:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-21 05:35:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6434327","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6434327","identity":"rs-6434327","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}