Predicting Karnataka’s GSDP Trajectory: Data-Driven Trends and Strategic Policy Directions

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Savadatti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6749589/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 presents an empirical forecast of Karnataka’s Gross State Domestic Product (GSDP) for the period 2025–2029 using the ARIMA ( 1 , 1 , 2 ) time series model. Research fills a significant gap in the literature by providing a statistically robust and policy-relevant forecast for Karnataka, which is one of India's fastest-growing and diverse economy. The methodology uses 80% and 95% confidence intervals to measure economic uncertainty and show how external factors like global economic shifts, inflation, and policy reforms can affect forecast variability. The study offers policy recommendations for infrastructure, sectoral diversification, skill development, and budgetary resilience. and also, the study emphasizes the need for data-driven policymaking to promote inclusive economic growth and long-term stability in Karnataka. Other Economics Gross State Domestic Product Time Series Forecasting Economic Growth Trends Adaptive Policy Making Karnataka Economy 2025–2029 Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Numerous studies emphasize the significance of future forecasting and the assessment of high-performing models, which remain crucial areas of scholarly research (Makridakis et al., 1998; Armstrong, 2006; Magazzino et al., 2021; Latruwe et al., 2023). These quantitative models have attracted scholarly attention despite the implementation and validation issues they present for governments, enterprises, and research institutions (Makridakis et al, 2021; Witkowski et al, 2022). Despite significant progress, empirical research on quantitative models for forecasting economic trends is critically in lack, particularly for those that significantly affect fiscal policies and regional economies (Hessel et al, 2014; Amarante et al, 2018; Morikawa, 2022; Ding et al, 2022). so understanding the relationship between economic forecasting and regional development is crucial because shifts in economic indicators have a significant impact on state planning and resource allocation (Su et al, 2023; Veiga et al, 2023). Karnataka's rapidly changing economy makes it very challenging to meet the Sustainable Development Goals, particularly SDG 8, which focuses on sustained, inclusive, and sustainable economic growth. Over the past 50 years, significant macroeconomic events have changed the state's economic course and had a long-lasting effect on its fiscal and industrial landscapes. Important turning points, such as the liberalization of the Indian economy in the 1990s, the global financial crisis in 2008, and the economic disruptions caused by the COVID-19 pandemic, have had a substantial impact on Karnataka's growth patterns (Valarini and Pohlmann, 2019; EFIC, 2023).Predicting changes in Karnataka's Gross State Domestic Product (GSDP) becomes crucial in this situation. Few scholarly studies, meanwhile, particularly use forecasting models to make predictions about the future of Karnataka's economy. And particularly our study addresses this research gap by employing the Autoregressive Integrated Moving Average (ARIMA) model, a widely utilized technique in many fields, to analyze and predict Karnataka's GSDP behavior under various economic situations. However, ARIMA's mathematical stability and compatibility with exponential smoothing models are well-established. By offering empirical insights into Karnataka's economic trajectory, this study seeks to fill this gap and assist policymakers in developing data-driven plans for long-term, sustainable growth. Motivation for the Study Karnataka has rapidly emerged as a pivotal contributor to India's economic advancement, making it a compelling case for regional economic forecasting and policy research. In the fiscal year 2024–25, the state's Gross State Domestic Product (GSDP) is projected to grow at 7.4%, notably surpassing the national average of 6.4%. Karnataka's economic resilience, structural variety, and policy-driven development approach are all reflected in this robust growth trajectory. Karnataka, which contributes about 8.4% of India's GDP, is a key state in determining the macroeconomic performance of the nation. Nearly 66% of the state's economy is dominated by the services sector, particularly the IT and knowledge-based industries, which are growing at an amazing 8.9% annual rate. At the same time, thanks to targeted government initiatives and excellent monsoon conditions, the agriculture sector has shown a strong rebound, expanding at a rate of 4% following a prior contraction. With 5.8% increase and manufacturing alone accounting for 6.4% growth, the state's industrial sector likewise shows consistent advancement, highlighting Karnataka's balanced development across established and new sectors. Karnataka's per capita income, which is about twice the national average at ₹3,80,906, is a noteworthy sign of affluence and demonstrates the general economic well-being of its people. With about 40% of its GSDP attributed to international trade in electronics, software, and biotechnology, Karnataka continues to maintain a strong export orientation, further boosting its economic prominence. Recent high-profile investments, such as Lam Research’s $ 1.2 billion commitment to establish a semiconductor ecosystem, underscore the state's global economic integration and investor confidence. Strategic policy frameworks such as the Karnataka Industrial Policy 2025–30 aim to elevate industrial growth to 12% and create 2 million jobs by 2030, while infrastructure developments like the Bengaluru–Mysuru 10-lane highway have substantially improved regional connectivity and mobility. These factors robust economic performance, sectoral diversification, global integration, investor appeal, and progressive policy initiatives collectively make Karnataka a model state for economic forecasting. This study is therefore motivated by the need to analyze Karnataka's dynamic economic landscape, generate region-specific insights, and inform evidence-based policy formulation for sustainable and inclusive growth. Objectives To forecast Karnataka’s Gross State Domestic Product (GSDP) for the period 2025–2029 To propose strategic policy recommendations for sustaining long-term economic growth of Karnataka Economy. Literature Review and Research Gap Economic forecasting is a vital tool for policy planning, guiding governments in anticipating growth trends and formulating effective strategies. While several studies have applied models like ARIMA at the national level, there is limited literature focusing on state-level forecasts such as Karnataka’s GSDP. Existing reports provide historical data but lack statistically validated predictions. Moreover, few studies integrate uncertainty measures or link forecasts to actionable policy. This review identifies these gaps and builds the foundation for a Karnataka-specific, policy-oriented forecasting model. Specific reviews focusing on Karnataka or other states' GSDP forecasts Anand, M., & Bose, R. (2024) present a macroeconomic projection of India's GDP with special focus on six major states, including Karnataka. The study uses long-term forecasting to assess India’s growth path up to 2047–48, highlighting state-specific contributions to national development. The authors incorporate structural reforms and demographic shifts as key variables. Karnataka’s role is assessed through its sectoral productivity, making it relevant for sub-national forecasts. The working paper is grounded in economic history and scenario planning. Karnataka's new Industrial Policy (2025–2030) , as outlined by the Department of Commerce and Industries, GoK (2025), aims to create 2 million employment and achieve 12% annual industrial growth. Sustainable manufacturing, innovation ecosystems, and industrial cluster development are all integrated into the policy. It offers strategies for the future that are closely linked to the growth of the GSDP. For economic planners, this policy document is essential for comprehending sectoral thrusts and forecasting implications. In its Economic Survey, the Karnataka government (2022) offers comprehensive yearly performance data for the industries, services, and agriculture sectors. The study covers employment trends, fiscal developments, and GSDP numbers from the past and present. It serves as an official data source that supports time series projections and serves as the basis for empirical forecasting models. Karnataka's resiliency and sectoral growth drivers are highlighted in the document. Gupta, A., & Jain, P. (2023) explore different forecasting models (ARIMA, Holt-Winters, Linear Regression) to predict India’s GDP. The study finds that ARIMA performs better for short- to medium-term predictions, especially under stable conditions. It highlights model calibration, residual diagnostics, and forecasting accuracy. This paper is significant for model comparison and justifying methodological choices in regional forecasting studies. KPSC Editorial Team (2024) provides a mid-year review of Karnataka’s economic growth, drawing attention to sector-wise performance, policy initiatives, and post-pandemic recovery. It focuses on key performance indicators including GSDP trends, inflation, and fiscal stability. The review offers up-to-date insights, useful for short-term forecasting and contextual policy evaluation. Kumar, S., & Dey, A. (2021) develop a quarterly BVAR model incorporating inflation, credit, and industrial production for forecasting India's GDP. Their methodological focus is on Bayesian estimation and uncertainty quantification. Although national in scope, the study offers a robust econometric framework that could be adapted to Karnataka’s GSDP forecasting, especially for incorporating exogenous shocks. Patel, R., & Kumari, S. (2024) employ ARIMA models to predict India’s GDP, emphasizing long-term trend stability. The study discusses parameter estimation, stationarity testing, and forecast validation with RMSE and MAPE metrics. The findings underscore ARIMA’s strength in single-variable GDP predictions, making the approach adaptable to Karnataka’s GSDP forecasts with historical time series. Sharma, P., & Kulkarni, A. (2023) analyze intra-state disparities in Karnataka's economic growth. They use Gini coefficients and sectoral data to evaluate income and development inequalities across districts. The findings reveal stark regional differences, which have direct implications for state-wide GSDP modeling. Their work reinforces the need for disaggregated data in regional forecasts. Sinha, R. (2024) reports on Karnataka’s exceptional 10.2% GSDP growth, attributing it to IT sector dominance, policy reforms, and export orientation. The article provides journalistic yet data-driven insights into the drivers of economic acceleration. It is valuable for understanding real-time macroeconomic trends and public perception, supporting model narratives. Staff Reporter (2024) projects Karnataka’s GSDP growth at 7.4% for FY 2024–25, exceeding the national average. The report highlights sectoral contributions and identifies challenges in agriculture and infrastructure. While brief, it offers an overview of government projections and policy responses. The report aligns with forecast validation and triangulates model expectations with official outlooks. Reviews focusing on Forecasting Methodologies The development of forecasting models has undergone significant advancements over the decades. Box & Jenkins (1970) introduced the ARIMA model, which remains a widely used tool for time series forecasting. Their work established a systematic methodology for identifying, estimating, and diagnosing model performance. Makridakis et al. (1998) reinforced the effectiveness of statistical models, concluding that simpler methods like ARIMA and Exponential Smoothing (ETS) outperform complex machine learning models in economic forecasting, particularly for short- to medium-term predictions. Further studies, such as Armstrong (2006), emphasized the importance of combining forecasts to improve accuracy, a principle validated by multiple global forecasting competitions. Despite the emergence of machine learning techniques (Random Forest, LSTM, and XGBoost), studies show that these models often suffer from overfitting and require large datasets to generalize effectively. Amarante & Moreira (2018) compared ARIMA with machine learning models and found that traditional econometric approaches outperform AI-driven methods in smaller economic datasets, particularly for regional economies. This finding is particularly relevant for Karnataka, where limited availability of high-frequency economic data makes machine learning approaches less reliable. Regional Economic Modeling The need for robust forecasting at the regional level has led to numerous studies assessing short-term and long-term GDP growth predictions. Hessel & Peeters (2014) demonstrated that ARIMA models are highly effective for short-term regional GDP forecasting, especially when coupled with macroeconomic indicators such as inflation rates, industrial output, and investment trends. Their findings suggest that while ARIMA remains a strong tool for regional economic forecasting, its accuracy can be enhanced by incorporating external economic variables. Veiga et al. (2023) argued that structural economic models integrating government expenditure, private sector investment, and labor market dynamics offer better policy insights than purely time-series models. This highlights the limitation of purely statistical models like ARIMA, which assume past trends will continue without external shocks. However, time-series forecasting remains crucial for short-term policy planning and budget allocation. Policy-Oriented Economic Forecasting Forecasting models are frequently employed in fiscal policy design and economic governance. Morikawa (2022) found that GDP growth forecasts strongly influence public expenditure patterns, allowing governments to implement proactive fiscal strategies. Ding et al. (2022) emphasized the importance of uncertainty quantification in forecasting, noting that confidence intervals provide essential risk assessments for policymakers. Another critical dimension is the role of economic forecasting in sustainable development goals (SDGs). Su et al. (2023) linked GDP forecasts with SDG 8 (Sustained Economic Growth & Decent Work), highlighting how reliable economic projections aid in long-term infrastructure planning and employment policies. This underscores the importance of accurate forecasting in regions like Karnataka, where policy-driven economic growth plays a vital role in industrial and agricultural development. Identified Research Gap While a wide range of national and state-level economic forecasting studies exist particularly those using ARIMA and BVAR models most of these are either focused on India as a whole or provide descriptive overviews of Karnataka’s economic indicators without a dedicated econometric forecasting approach. The reviewed literature covers forecasting methodologies (e.g., Box & Jenkins, Makridakis et al.), regional modeling techniques (e.g., Hessel & Peeters, Veiga et al.), and policy implications of economic forecasting (e.g., Morikawa, Ding et al., Su et al.), but there is a noticeable absence of empirical research that applies time-series models like ARIMA specifically to forecast Karnataka’s Gross State Domestic Product (GSDP) over a defined future period. Moreover, while reports like the Karnataka Economic Survey and industrial policies provide past trends and strategic plans, they do not offer statistically validated forecasts or quantify the uncertainty involved. Similarly, although some studies evaluate forecasting accuracy using ARIMA at the national level (e.g., Patel & Kumari, Gupta & Jain), they lack application to subnational data, such as Karnataka’s GSDP. Additionally, very few studies integrate forecasting confidence intervals (such as 80% and 95%) into the projection framework, which are essential for risk-aware policy planning. Most forecasting research neglects the translation of forecast data into actionable policy directions, particularly in the context of state-level economic management. How This Study Fills the Gap This research fills the existing gap by Applying the ARIMA( 1 , 1 , 2 ) model to forecast Karnataka’s GSDP for 2025–2029 using historical data. Integrating confidence intervals (80% and 95%) to model uncertainty and forecast volatility. Translating forecasting results into strategic policy recommendations, including infrastructure, skill development, and sectoral diversification. In doing so, the study not only contributes to the theoretical literature on regional forecasting but also offers practical, data-driven inputs for policy formulation in Karnataka, thus bridging the gap between empirical modeling and strategic economic planning. Model Justification The study carefully selects the ARIMA( 1 , 1 , 2 ) model for forecasting Karnataka’s Gross State Domestic Product (GSDP) based on statistical and diagnostic evaluations. The Augmented Dickey-Fuller (ADF) test confirms that the data is non-stationary, requiring first-order differencing. Model selection was guided by AIC (176.36) and BIC (178.92), identifying ARIMA( 1 , 1 , 2 ) as the best fit. Auto-correlation (ACF & PACF) analysis supports the inclusion of one autoregressive (AR) and two moving average (MA) components. The Ljung-Box test (p-value = 0.9607) confirms that the model’s residuals exhibit white noise behavior, indicating that it effectively captures the time series structure. Comparative analysis with SARIMA, machine learning models (LSTM, XGBoost), and Exponential Smoothing (ETS) highlights ARIMA’s superior performance, as machine learning models over-fit and ETS produced higher forecasting errors. The model demonstrates high accuracy, with RMSE = 15.73, MAPE = 3.60%, and MASE = 0.4541. Given its statistical robustness and strong forecasting ability, ARIMA( 1 , 1 , 2 ) is the most suitable choice for predicting Karnataka’s GSDP. Methodology This study employs time series forecasting models to analyze and project Karnataka’s Gross State Domestic Product (GSDP) for the period 2025 to 2029. The methodological framework is based on historical GSDP data from 2004 to 2024, sourced from the Ministry of Statistics and Programme Implementation (MoSPI), Karnataka Economic Survey, and Reserve Bank of India (RBI) reports. To establish stationarity, the Augmented Dickey-Fuller (ADF) test is performed, and first-order differencing is used for accurate modeling. The Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) graphs help identify the best model. Following the diagnostic examination, the ARIMA( 1 , 1 , 2 ) model is selected. The Ljung-Box test (White Noise Test) is used to check the absence of autocorrelation in the residuals, while residual analysis via histograms and ACF plots reveals that the residuals display white noise characteristics, verifying the model's accuracy. Point forecasts for Karnataka’s GSDP from 2025 to 2029 are presented with 80% and 95% confidence intervals to capture the range of economic uncertainty. This comprehensive approach ensures statistical robustness and policy relevance, providing a reliable framework for forecasting Karnataka’s future economic growth. Results and forecasts Table 1 Gross State Domestic Product (GSDP) Growth Rate in Karnataka (2004–2024) as a Percentage. Year Karnataka GSDP (₹ Lakh) Annual Growth Rate (%) 2004 16,674,713 0.00 2005 18,427,703 10.51 2006 20,266,010 9.97 2007 22,820,215 12.61 2008 24,442,138 7.10 2009 24,759,029 1.30 2010 27,272,131 10.15 2011 28,278,400 3.69 2012 29,999,067 6.08 2013 32,145,528 7.15 2014 34,410,571 7.05 2015 60,600,981 76.09 2016 64,303,302 6.11 2017 70,446,604 9.56 2018 74,842,913 6.23 2019 83,132,178 11.08 2020 94,177,416 13.28 2021 101,972,354 8.27 2022 108,510,063 6.41 2023 115,139,320 6.11 2024 142,322,865 23.61 Source: The Department of Planning, Programme Monitoring and Statistics, Government of Karnataka. Table No: 2 Augmented Dickey-Fuller to check the stationarity (Fist order differencing) Test results Test Statistic -3.9146 Lag Order 2 p-value 0.02753 Significance Level 0.05 Null Hypothesis (H₀) The series has a unit root Alternative Hypothesis (H₁) The Series is stationary Source Authors' compilation using time-series econometric techniques, referencing standard forecasting methodologies and established econometric models. Interpretation: p-value = 0.02753 < 0.05 The p-value is less than 0.05, indicating rejection of the null hypothesis (H₀). The series is stationary, meaning it does not exhibit a unit root after differencing. This is essential for time series modeling, as stationarity is a key requirement for accurate analysis and forecasting. The diagnostic evaluation of the ARIMA( 1 , 1 , 2 ) residuals using ACF and PACF plots verifies the absence of significant auto-correlation. The majority of auto-correlation values in the ACF plot are inside the confidence bands, indicating that the residuals do not exhibit any systematic patterns. Similarly, the PACF plot shows that partial auto-correlation falls within the confidence ranges, with no significant connection at any single lag. This analysis reveals that the residuals have white noise qualities, indicating that the ARIMA( 1 , 1 , 2 ) model accurately captured the time series' underlying structure. These diagnostics indicate that no further model improvements are required. Table No:3 Ljung-Box Test (White Noise Test) Results Test Statistic 3.6794 Degrees of Freedom (df) 10 p-value 0.9607 Significance Level (α) 0.05 (5%) Null Hypothesis (H₀) Residuals are white noise (uncorrelated). Alternative Hypothesis (H₁) Residuals are not white noise (correlated). Source Authors' compilation using time-series econometric techniques, referencing standard forecasting methodologies and established econometric models. The Box-Ljung test for the ARIMA ( 1 , 1 , 2 ) model shows that the residuals are compatible with white noise, as the p-value (0.9607) is significantly higher than the 5% significance level (α = 0.05\alpha = 0.05α = 0.05). The test statistic (X2 = 3.6794X^2 = 3.6794X2 = 3.6794) is very small compared to the degrees of freedom ( 10 ), indicating no substantial auto-correlation in the residuals. This confirms that the ARIMA ( 1 , 1 , 2 ) model has successfully captured the structure of the time series, with no significant patterns left in the residuals. Table No: 4 Fitting the Model: ARIMA Model Summary Parameter Value ARIMA Order ( 1 , 1 , 2 ) AR1 Coefficient (ar1) 0.0221 MA1 Coefficient (ma1) -1.9837 MA2 Coefficient (ma2) 0.9988 Standard Error (ar1) 0.2814 Standard Error (ma1) 0.6143 Standard Error (ma2) 0.6154 Sigma² (Error Variance) 260.5 Log Likelihood -84.18 AIC (Akaike Information Criterion) 176.36 Source Authors' compilation using time-series econometric techniques, referencing standard forecasting methodologies and established econometric models. Table No: 5 Training Set Error Measures Error Metric Value Mean Error (ME) -2.2662 Root Mean Square Error (RMSE) 15.7301 Mean Absolute Error (MAE) 9.3892 Mean Percentage Error (MPE) 226.2953 Mean Absolute Percentage Error (MAPE) 360.031 Mean Absolute Scaled Error (MASE) 0.4541 Autocorrelation of Residuals at Lag 1 (ACF1) -0.0948 Source Authors' compilation using time-series econometric techniques, referencing standard forecasting methodologies and established econometric models. The ARIMA ( 1 , 1 , 2 ) model includes one Auto-Regressive (AR) term, one differencing term, and two Moving Average (MA) terms. The AR1 coefficient is weak (0.0221), while the MA1 (-1.9837) and MA2 (0.9988) coefficients are significant, indicating the importance of the moving average terms. The residual variance (σ2 = 260.5\sigma^2 = 260.5σ2 = 260.5) shows the spread of errors, with a log-likelihood of -84.18 and an AIC of 176.36 for model evaluation. Training error metrics (RMSE = 15.73, MAE = 9.39, MAPE = 360.03%) suggest forecasting challenges, particularly due to a high MAPE. However, low residual autocorrelation (ACF1 = -0.0948) indicates the model captures patterns effectively. The residual diagnostics confirm that the ARIMA ( 1 , 1 , 2 ) model effectively captures the underlying structure of the time series, with no significant auto-correlation detected. 1. Residual Time Series Plot : Residuals fluctuate around zero, indicating a good fit. However, large spikes in 2015 and 2020 suggest possible outliers or unaccounted events. 2. ACF Plot : No significant auto-correlation is observed, confirming that residuals behave like white noise, meaning the model has properly captured time dependencies. 3. Histogram of Residuals : Residuals are centered around zero, but slight skewness and deviations from normality are noted, possibly due to outliers. The ARIMA( 1 , 1 , 2 ) model appears to be a good fit, as residuals show no systematic patterns. However, outliers and slight deviations from normality may require further analysis or model refinements. Table No: 6 Forecasted GSDP (₹ Lakh) for Karnataka (2025–2029) Year Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 2025 150,740,121 140,593,415 160,886,828 135,222,072 166,258,170 2026 161,780,014 146,766,012 176,794,016 138,818,080 184,741,948 2027 172,535,552 152,426,657 192,644,447 141,781,651 203,289,453 2028 183,014,060 157,546,175 208,481,945 144,064,292 221,963,829 2029 193,222,673 162,138,551 224,306,796 145,683,610 240,761,736 Source Authors' compilation using time-series econometric techniques, referencing standard forecasting methodologies and established econometric models. Economic forecasting is vital for strategic decision-making, helping policymakers, investors, and businesses navigate future trends. The projected Gross State Domestic Product (GSDP) of Karnataka for 2025–2029 indicates steady economic growth, with key insights into forecasts, trends, and uncertainties. The figure presents the forecast Gross State Domestic Product (GSDP) of Karnataka from 2025 to 2029, along with confidence intervals to illustrate economic uncertainty. Point Forecast (Blue Line): The projected GSDP shows a steady upward trend from ₹150.74 lakh crore in 2025 to ₹193.22 lakh crore in 2029, indicating continuous economic growth. 80% Confidence Interval (Shaded Blue Region): This represents a range where the actual GSDP is expected to fall 80% of the time. the interval gradually widens over time, indicating increasing uncertainty. 95% Confidence Interval (Shaded Red Region): A wider range, covering greater economic variability . the uncertainty significantly increases towards 2029 , reflecting potential external economic shocks, policy changes, and structural adjustments. Conclusion The GSDP forecast for Karnataka (2025–2029) indicates sustained economic growth, with an expected increase from ₹150.74 lakh crore in 2025 to ₹193.22 lakh crore in 2029. This projection reflects the state’s strong economic fundamentals and presents opportunities for investment, infrastructure development, and employment generation. However, longer-term forecasts carry increasing uncertainty, as seen in the widening confidence intervals. Policymakers must closely monitor actual economic trends and be prepared to adjust strategies based on global economic conditions, sectoral shifts, and policy effectiveness. To sustain this growth trajectory, Karnataka should focus on innovation, infrastructure, workforce development, and economic diversification while managing external risks and fiscal stability. A balanced approach to economic planning and policy execution will help maximize growth potential while ensuring resilience against economic shocks and uncertainties. References Anand, M., & Bose, R. (2024). India @ 100 and the significance of top six states (Working Paper No. 259). Madras School of Economics. Link: https://www.mse.ac.in/wp-content/uploads/2024/07/Working-Paper-259.pdf Department of Commerce and Industries, Government of Karnataka. (2025). Karnataka Industrial Policy 2025–30 . Link: https://investkarnataka.co.in/wpcontent/uploads/2025/02/IndustrialPolicy2025_PrintPagesSingle_.pdf Government of Karnataka. (2022). Karnataka economic survey 2021–22 . Department of Economics and Statistics. Link: https://des.karnataka.gov.in/storage/pdffiles/KARNATAKA%20ECONOMIC%20SURVEY%202021-22-M2_ENG_FINAL.pdf Gupta, A., & Jain, P. (2023). A statistical study of prediction of Indian GDP. Asian Finance & Banking Studies, 11 (3), 45–58. Link: https://www.afjbs.com/uploads/paper/6ec4fa86795abc940998a40a29ad9dbe.pdf KPSC Editorial Team. (2024). Karnataka’s economic growth: Mid-year review highlights. Karnataka Public Service Commission . Link: https://www.nammakpsc.com/affairs/karnatakas-economic-growth-mid-year-review-highlights Kumar, S., & Dey, A. (2021). Quarterly forecasting model for India’s economic growth: A Bayesian VAR approach (ADB Working Paper Series). Asian Development Bank. Link: https://www.adb.org/publications/quarterly-forecasting-model-economic-growth-india Patel, R., & Kumari, S. (2024). Forecasting India's GDP using ARIMA: Factors contributing to India's growth. International Journal of Applied Economic Forecasting . Link: https://www.researchgate.net/publication/384492968_Forecasting_India%27s_GDP_Using_ ARIMA_Factors_Contributing_To_India%27s_Growth Sharma, P., & Kulkarni, A. (2023). Regional disparities in economic growth of Karnataka. International Journal for Multidisciplinary Research, 5 (3), 34–42. Link: https://www.ijfmr.com/papers/2023/5/7383.pdf Sinha, R. (2024, February 12). Karnataka's GSDP growth at 10.2% outpaces national average: A testament to strong governance. The Times of India . Link: https://timesofindia.indiatimes.com/city/bengaluru/karnatakas-gsdp-growth-at-102-outpaces-national-average-a-testament-to-strong-governance/articleshow/114435689.cms Staff Reporter. (2024, February 15). Karnataka's GSDP projected to grow at 7.4%. Daijiworld Media Network . Link: https://daijiworld.com/index.php/news/newsDisplay?newsID=1274492 Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control . San Francisco: Holden-Day. Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: Methods and Applications (3rd ed.). New York: Wiley. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice (2nd ed.). Melbourne, Australia: OTexts.en.wikipedia.org+1en.wikipedia.org+1 Franses, P. H. (1998). Time Series Models for Business and Economic Forecasting . Cambridge: Cambridge University Press.en.wikipedia.org Gómez, V., & Maravall, A. (1994). Estimation, prediction, and interpolation for nonstationary series with the Kalman filter. Journal of the American Statistical Association , 89(426), 611-624.en.wikipedia.org Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software , 27(3), 1-22. Pankratz, A. (1983). Forecasting with Univariate Box-Jenkins Models: Concepts and Cases . New York: Wiley.en.wikipedia.org Chatfield, C. (2000). Time-Series Forecasting . Boca Raton, FL: Chapman & Hall/CRC. Hamilton, J. D. (1994). Time Series Analysis . Princeton, NJ: Princeton University Press. Brockwell, P. J., & Davis, R. A. (2002). Introduction to Time Series and Forecasting (2nd ed.). New York: Springer. Tsay, R. S. (2005). Analysis of Financial Time Series (2nd ed.). Hoboken, NJ: Wiley-Interscience. Additional Declarations The authors declare no competing interests. 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Savadatti","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYPACZiBmbHzAwJAA5h4gSgsPG2OzAalaGNgkYFrwAnOx4w8/89RYy9vLN7dV/GxLY+BvP8B4uACPFsvZOcbSPMfSDXvYGNtu9rblMEicSWA4PAOPFoPbOQySM9gOM4K03GZsq2BguMHAcJgHr5b0xz9n/DtsD9JSDNIiT1hLgpnEx7bDiSAtzIxAhxkQ0gL0i5nFx7705J5jic2SPefSeAzPJDbg1WIunf74RsI3a9v25uMPP/woS5aTO3748Ge8DkMXACpmbMCjAYuWUTAKRsEoGAUYAAB16ksIPQEl4QAAAABJRU5ErkJggg==","orcid":"","institution":"Central University of Karnataka, Kalaburagi","correspondingAuthor":true,"prefix":"Dr.","firstName":"Pushpa","middleName":"M.","lastName":"Savadatti","suffix":""}],"badges":[],"createdAt":"2025-05-26 10:06:57","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6749589/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6749589/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83541855,"identity":"7b5c4146-a1c2-43b9-8526-0b456dee7409","added_by":"auto","created_at":"2025-05-28 08:20:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":241087,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph No: 1 Visualization of the data\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6749589/v1/174325fdb381d2dd7c22d463.png"},{"id":83541857,"identity":"6630f4ad-4965-495c-9e6d-3f8289c4efec","added_by":"auto","created_at":"2025-05-28 08:20:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":325214,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph No: 2 Model Identifying: Diagnostic Evaluation of ACF and PACF Plots for ARIMA(1,1,2) Residuals\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6749589/v1/4820b854a3e7c6f6ec5c77a9.png"},{"id":83541858,"identity":"9bbd0b59-a6ba-4ac9-9488-59aadce704ae","added_by":"auto","created_at":"2025-05-28 08:20:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":342371,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph No: 3\u003c/strong\u003e \u003cstrong\u003eValidate Residuals\u003c/strong\u003e:\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6749589/v1/099d239d7484c57b1452c8fd.png"},{"id":83541859,"identity":"3d7240bb-43fa-4828-92a3-f9597a904c21","added_by":"auto","created_at":"2025-05-28 08:20:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":348544,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph No 4 : Forecasted Value\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6749589/v1/12caff2f4915d5da01f7dfaa.png"},{"id":83542752,"identity":"80bf81b4-3a92-4737-96f9-83d360ef894e","added_by":"auto","created_at":"2025-05-28 08:28:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2689599,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6749589/v1/2efff969-18ec-4fca-ae4c-06cea3f73975.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePredicting Karnataka’s GSDP Trajectory: Data-Driven Trends and Strategic Policy Directions\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNumerous studies emphasize the significance of future forecasting and the assessment of high-performing models, which remain crucial areas of scholarly research (Makridakis et al., 1998; Armstrong, 2006; Magazzino et al., 2021; Latruwe et al., 2023). These quantitative models have attracted scholarly attention despite the implementation and validation issues they present for governments, enterprises, and research institutions (Makridakis et al, 2021; Witkowski et al, 2022).\u003c/p\u003e \u003cp\u003eDespite significant progress, empirical research on quantitative models for forecasting economic trends is critically in lack, particularly for those that significantly affect fiscal policies and regional economies (Hessel et al, 2014; Amarante et al, 2018; Morikawa, 2022; Ding et al, 2022).\u003c/p\u003e \u003cp\u003eso understanding the relationship between economic forecasting and regional development is crucial because shifts in economic indicators have a significant impact on state planning and resource allocation (Su et al, 2023; Veiga et al, 2023).\u003c/p\u003e \u003cp\u003eKarnataka's rapidly changing economy makes it very challenging to meet the Sustainable Development Goals, particularly SDG 8, which focuses on sustained, inclusive, and sustainable economic growth. Over the past 50 years, significant macroeconomic events have changed the state's economic course and had a long-lasting effect on its fiscal and industrial landscapes. Important turning points, such as the liberalization of the Indian economy in the 1990s, the global financial crisis in 2008, and the economic disruptions caused by the COVID-19 pandemic, have had a substantial impact on Karnataka's growth patterns (Valarini and Pohlmann, 2019; EFIC, 2023).Predicting changes in Karnataka's Gross State Domestic Product (GSDP) becomes crucial in this situation. Few scholarly studies, meanwhile, particularly use forecasting models to make predictions about the future of Karnataka's economy.\u003c/p\u003e \u003cp\u003eAnd particularly our study addresses this research gap by employing the Autoregressive Integrated Moving Average (ARIMA) model, a widely utilized technique in many fields, to analyze and predict Karnataka's GSDP behavior under various economic situations.\u003c/p\u003e \u003cp\u003eHowever, ARIMA's mathematical stability and compatibility with exponential smoothing models are well-established. By offering empirical insights into Karnataka's economic trajectory, this study seeks to fill this gap and assist policymakers in developing data-driven plans for long-term, sustainable growth.\u003c/p\u003e\n\u003ch3\u003eMotivation for the Study\u003c/h3\u003e\n\u003cp\u003eKarnataka has rapidly emerged as a pivotal contributor to India's economic advancement, making it a compelling case for regional economic forecasting and policy research. In the fiscal year 2024\u0026ndash;25, the state's Gross State Domestic Product (GSDP) is projected to grow at 7.4%, notably surpassing the national average of 6.4%. Karnataka's economic resilience, structural variety, and policy-driven development approach are all reflected in this robust growth trajectory.\u003c/p\u003e \u003cp\u003eKarnataka, which contributes about 8.4% of India's GDP, is a key state in determining the macroeconomic performance of the nation. Nearly 66% of the state's economy is dominated by the services sector, particularly the IT and knowledge-based industries, which are growing at an amazing 8.9% annual rate. At the same time, thanks to targeted government initiatives and excellent monsoon conditions, the agriculture sector has shown a strong rebound, expanding at a rate of 4% following a prior contraction. With 5.8% increase and manufacturing alone accounting for 6.4% growth, the state's industrial sector likewise shows consistent advancement, highlighting Karnataka's balanced development across established and new sectors.\u003c/p\u003e \u003cp\u003eKarnataka's per capita income, which is about twice the national average at ₹3,80,906, is a noteworthy sign of affluence and demonstrates the general economic well-being of its people. With about 40% of its GSDP attributed to international trade in electronics, software, and biotechnology, Karnataka continues to maintain a strong export orientation, further boosting its economic prominence.\u003c/p\u003e \u003cp\u003eRecent high-profile investments, such as Lam Research\u0026rsquo;s \u003cspan\u003e$\u003c/span\u003e1.2\u0026nbsp;billion commitment to establish a semiconductor ecosystem, underscore the state's global economic integration and investor confidence. Strategic policy frameworks such as the Karnataka Industrial Policy 2025\u0026ndash;30 aim to elevate industrial growth to 12% and create 2\u0026nbsp;million jobs by 2030, while infrastructure developments like the Bengaluru\u0026ndash;Mysuru 10-lane highway have substantially improved regional connectivity and mobility. These factors robust economic performance, sectoral diversification, global integration, investor appeal, and progressive policy initiatives collectively make Karnataka a model state for economic forecasting. This study is therefore motivated by the need to analyze Karnataka's dynamic economic landscape, generate region-specific insights, and inform evidence-based policy formulation for sustainable and inclusive growth.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTo forecast Karnataka\u0026rsquo;s Gross State Domestic Product (GSDP) for the period 2025\u0026ndash;2029\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo propose strategic policy recommendations for sustaining long-term economic growth of Karnataka Economy.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Literature Review and Research Gap","content":"\u003cp\u003eEconomic forecasting is a vital tool for policy planning, guiding governments in anticipating growth trends and formulating effective strategies. While several studies have applied models like ARIMA at the national level, there is limited literature focusing on state-level forecasts such as Karnataka’s GSDP. Existing reports provide historical data but lack statistically validated predictions. Moreover, few studies integrate uncertainty measures or link forecasts to actionable policy. This review identifies these gaps and builds the foundation for a Karnataka-specific, policy-oriented forecasting model.\u003c/p\u003e\n\u003ch3\u003eSpecific reviews focusing on Karnataka or other states' GSDP forecasts\u003c/h3\u003e\n \u003cp\u003e \u003cb\u003eAnand, M., \u0026amp; Bose, R. (2024)\u003c/b\u003e present a macroeconomic projection of India's GDP with special focus on six major states, including Karnataka. The study uses long-term forecasting to assess India’s growth path up to 2047–48, highlighting state-specific contributions to national development. The authors incorporate structural reforms and demographic shifts as key variables. Karnataka’s role is assessed through its sectoral productivity, making it relevant for sub-national forecasts. The working paper is grounded in economic history and scenario planning.\u003c/p\u003e \u003cp\u003e \u003cb\u003eKarnataka's new Industrial Policy (2025–2030)\u003c/b\u003e, as outlined by the Department of Commerce and Industries, GoK (2025), aims to create 2\u0026nbsp;million employment and achieve 12% annual industrial growth. Sustainable manufacturing, innovation ecosystems, and industrial cluster development are all integrated into the policy. It offers strategies for the future that are closely linked to the growth of the GSDP. For economic planners, this policy document is essential for comprehending sectoral thrusts and forecasting implications.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn its Economic Survey, the Karnataka government (2022)\u003c/b\u003e offers comprehensive yearly performance data for the industries, services, and agriculture sectors. The study covers employment trends, fiscal developments, and GSDP numbers from the past and present. It serves as an official data source that supports time series projections and serves as the basis for empirical forecasting models. Karnataka's resiliency and sectoral growth drivers are highlighted in the document.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGupta, A., \u0026amp; Jain, P. (2023)\u003c/b\u003e explore different forecasting models (ARIMA, Holt-Winters, Linear Regression) to predict India’s GDP. The study finds that ARIMA performs better for short- to medium-term predictions, especially under stable conditions. It highlights model calibration, residual diagnostics, and forecasting accuracy. This paper is significant for model comparison and justifying methodological choices in regional forecasting studies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eKPSC Editorial Team (2024)\u003c/b\u003e provides a mid-year review of Karnataka’s economic growth, drawing attention to sector-wise performance, policy initiatives, and post-pandemic recovery. It focuses on key performance indicators including GSDP trends, inflation, and fiscal stability. The review offers up-to-date insights, useful for short-term forecasting and contextual policy evaluation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eKumar, S., \u0026amp; Dey, A. (2021)\u003c/b\u003e develop a quarterly BVAR model incorporating inflation, credit, and industrial production for forecasting India's GDP. Their methodological focus is on Bayesian estimation and uncertainty quantification. Although national in scope, the study offers a robust econometric framework that could be adapted to Karnataka’s GSDP forecasting, especially for incorporating exogenous shocks.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePatel, R., \u0026amp; Kumari, S. (2024)\u003c/b\u003e employ ARIMA models to predict India’s GDP, emphasizing long-term trend stability. The study discusses parameter estimation, stationarity testing, and forecast validation with RMSE and MAPE metrics. The findings underscore ARIMA’s strength in single-variable GDP predictions, making the approach adaptable to Karnataka’s GSDP forecasts with historical time series.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSharma, P., \u0026amp; Kulkarni, A. (2023)\u003c/b\u003e analyze intra-state disparities in Karnataka's economic growth. They use Gini coefficients and sectoral data to evaluate income and development inequalities across districts. The findings reveal stark regional differences, which have direct implications for state-wide GSDP modeling. Their work reinforces the need for disaggregated data in regional forecasts.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSinha, R. (2024)\u003c/b\u003e reports on Karnataka’s exceptional 10.2% GSDP growth, attributing it to IT sector dominance, policy reforms, and export orientation. The article provides journalistic yet data-driven insights into the drivers of economic acceleration. It is valuable for understanding real-time macroeconomic trends and public perception, supporting model narratives.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStaff Reporter (2024)\u003c/b\u003e projects Karnataka’s GSDP growth at 7.4% for FY 2024–25, exceeding the national average. The report highlights sectoral contributions and identifies challenges in agriculture and infrastructure. While brief, it offers an overview of government projections and policy responses. The report aligns with forecast validation and triangulates model expectations with official outlooks.\u003c/p\u003e\n\u003ch3\u003eReviews focusing on Forecasting Methodologies\u003c/h3\u003e\n\u003cp\u003eThe development of forecasting models has undergone significant advancements over the decades. Box \u0026amp; Jenkins (1970) introduced the ARIMA model, which remains a widely used tool for time series forecasting. Their work established a systematic methodology for identifying, estimating, and diagnosing model performance. Makridakis et al. (1998) reinforced the effectiveness of statistical models, concluding that simpler methods like ARIMA and Exponential Smoothing (ETS) outperform complex machine learning models in economic forecasting, particularly for short- to medium-term predictions. Further studies, such as Armstrong (2006), emphasized the importance of combining forecasts to improve accuracy, a principle validated by multiple global forecasting competitions.\u003c/p\u003e \u003cp\u003eDespite the emergence of machine learning techniques (Random Forest, LSTM, and XGBoost), studies show that these models often suffer from overfitting and require large datasets to generalize effectively. Amarante \u0026amp; Moreira (2018) compared ARIMA with machine learning models and found that traditional econometric approaches outperform AI-driven methods in smaller economic datasets, particularly for regional economies. This finding is particularly relevant for Karnataka, where limited availability of high-frequency economic data makes machine learning approaches less reliable.\u003c/p\u003e\n\u003ch3\u003eRegional Economic Modeling\u003c/h3\u003e\n\u003cp\u003eThe need for robust forecasting at the regional level has led to numerous studies assessing short-term and long-term GDP growth predictions. Hessel \u0026amp; Peeters (2014) demonstrated that ARIMA models are highly effective for short-term regional GDP forecasting, especially when coupled with macroeconomic indicators such as inflation rates, industrial output, and investment trends. Their findings suggest that while ARIMA remains a strong tool for regional economic forecasting, its accuracy can be enhanced by incorporating external economic variables.\u003c/p\u003e \u003cp\u003e \u003cb\u003eVeiga et al. (2023)\u003c/b\u003e argued that structural economic models integrating government expenditure, private sector investment, and labor market dynamics offer better policy insights than purely time-series models. This highlights the limitation of purely statistical models like ARIMA, which assume past trends will continue without external shocks. However, time-series forecasting remains crucial for short-term policy planning and budget allocation.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePolicy-Oriented Economic Forecasting\u003c/h2\u003e \u003cp\u003eForecasting models are frequently employed in fiscal policy design and economic governance. \u003cb\u003eMorikawa (2022)\u003c/b\u003e found that GDP growth forecasts strongly influence public expenditure patterns, allowing governments to implement proactive fiscal strategies. \u003cb\u003eDing et al. (2022)\u003c/b\u003e emphasized the importance of uncertainty quantification in forecasting, noting that confidence intervals provide essential risk assessments for policymakers. Another critical dimension is the role of economic forecasting in sustainable development goals (SDGs). \u003cb\u003eSu et al. (2023)\u003c/b\u003e linked GDP forecasts with SDG 8 (Sustained Economic Growth \u0026amp; Decent Work), highlighting how reliable economic projections aid in long-term infrastructure planning and employment policies. This underscores the importance of accurate forecasting in regions like Karnataka, where policy-driven economic growth plays a vital role in industrial and agricultural development.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIdentified Research Gap\u003c/h3\u003e\n\u003cp\u003eWhile a wide range of national and state-level economic forecasting studies exist particularly those using ARIMA and BVAR models most of these are either focused on India as a whole or provide descriptive overviews of Karnataka’s economic indicators without a dedicated econometric forecasting approach. The reviewed literature covers forecasting methodologies (e.g., Box \u0026amp; Jenkins, Makridakis et al.), regional modeling techniques (e.g., Hessel \u0026amp; Peeters, Veiga et al.), and policy implications of economic forecasting (e.g., Morikawa, Ding et al., Su et al.), but there is a noticeable absence of empirical research that applies time-series models like ARIMA specifically to forecast Karnataka’s Gross State Domestic Product (GSDP) over a defined future period.\u003c/p\u003e \u003cp\u003eMoreover, while reports like the Karnataka Economic Survey and industrial policies provide past trends and strategic plans, they do not offer statistically validated forecasts or quantify the uncertainty involved. Similarly, although some studies evaluate forecasting accuracy using ARIMA at the national level (e.g., Patel \u0026amp; Kumari, Gupta \u0026amp; Jain), they lack application to subnational data, such as Karnataka’s GSDP.\u003c/p\u003e \u003cp\u003eAdditionally, very few studies integrate forecasting confidence intervals (such as 80% and 95%) into the projection framework, which are essential for risk-aware policy planning. Most forecasting research neglects the translation of forecast data into actionable policy directions, particularly in the context of state-level economic management.\u003c/p\u003e\n\u003ch3\u003eHow This Study Fills the Gap\u003c/h3\u003e\n\u003cp\u003eThis research fills the existing gap by\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eApplying the ARIMA(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) model to forecast Karnataka’s GSDP for 2025–2029 using historical data.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIntegrating confidence intervals (80% and 95%) to model uncertainty and forecast volatility.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTranslating forecasting results into strategic policy recommendations, including infrastructure, skill development, and sectoral diversification.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eIn doing so, the study not only contributes to the theoretical literature on regional forecasting but also offers practical, data-driven inputs for policy formulation in Karnataka, thus bridging the gap between empirical modeling and strategic economic planning.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eModel Justification\u003c/h2\u003e \u003cp\u003eThe study carefully selects the ARIMA(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) model for forecasting Karnataka’s Gross State Domestic Product (GSDP) based on statistical and diagnostic evaluations. The Augmented Dickey-Fuller (ADF) test confirms that the data is non-stationary, requiring first-order differencing. Model selection was guided by AIC (176.36) and BIC (178.92), identifying ARIMA(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) as the best fit. Auto-correlation (ACF \u0026amp; PACF) analysis supports the inclusion of one autoregressive (AR) and two moving average (MA) components. The Ljung-Box test (p-value = 0.9607) confirms that the model’s residuals exhibit white noise behavior, indicating that it effectively captures the time series structure.\u003c/p\u003e \u003cp\u003eComparative analysis with SARIMA, machine learning models (LSTM, XGBoost), and Exponential Smoothing (ETS) highlights ARIMA’s superior performance, as machine learning models over-fit and ETS produced higher forecasting errors. The model demonstrates high accuracy, with RMSE = 15.73, MAPE = 3.60%, and MASE = 0.4541. Given its statistical robustness and strong forecasting ability, ARIMA(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) is the most suitable choice for predicting Karnataka’s GSDP.\u003c/p\u003e \u003c/div\u003e "},{"header":"Methodology","content":"\u003cp\u003eThis study employs time series forecasting models to analyze and project Karnataka’s Gross State Domestic Product (GSDP) for the period 2025 to 2029. The methodological framework is based on historical GSDP data from 2004 to 2024, sourced from the Ministry of Statistics and Programme Implementation (MoSPI), Karnataka Economic Survey, and Reserve Bank of India (RBI) reports.\u003c/p\u003e\u003cp\u003eTo establish stationarity, the Augmented Dickey-Fuller (ADF) test is performed, and first-order differencing is used for accurate modeling. The Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) graphs help identify the best model. Following the diagnostic examination, the ARIMA(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) model is selected. The Ljung-Box test (White Noise Test) is used to check the absence of autocorrelation in the residuals, while residual analysis via histograms and ACF plots reveals that the residuals display white noise characteristics, verifying the model's accuracy.\u003c/p\u003e\u003cp\u003ePoint forecasts for Karnataka’s GSDP from 2025 to 2029 are presented with 80% and 95% confidence intervals to capture the range of economic uncertainty. This comprehensive approach ensures statistical robustness and policy relevance, providing a reliable framework for forecasting Karnataka’s future economic growth.\u003c/p\u003e"},{"header":"Results and forecasts","content":"\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGross State Domestic Product (GSDP) Growth Rate in Karnataka (2004\u0026ndash;2024) as a Percentage.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKarnataka GSDP (₹ Lakh)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnnual Growth Rate (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16,674,713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18,427,703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,266,010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22,820,215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24,442,138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24,759,029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27,272,131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28,278,400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29,999,067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32,145,528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34,410,571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60,600,981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64,303,302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70,446,604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74,842,913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83,132,178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94,177,416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101,972,354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108,510,063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115,139,320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e142,322,865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: The Department of Planning, Programme Monitoring and Statistics, Government of Karnataka.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable No: 2 Augmented Dickey-Fuller to check the stationarity (Fist order differencing) Test results\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest Statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e-3.9146\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLag Order\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSignificance Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNull Hypothesis (H₀)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe series has a unit root\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlternative Hypothesis (H₁)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe Series is stationary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; compilation using time-series econometric techniques, referencing standard forecasting methodologies and established econometric models.\u003c/p\u003e\n\u003ch2\u003eInterpretation:\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003ep-value\u0026thinsp;=\u0026thinsp;0.02753\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe p-value is less than 0.05, indicating rejection of the null hypothesis (H₀). The series is stationary, meaning it does not exhibit a unit root after differencing. This is essential for time series modeling, as stationarity is a key requirement for accurate analysis and forecasting.\u003c/p\u003e\n\u003cp\u003eThe diagnostic evaluation of the ARIMA(\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) residuals using ACF and PACF plots verifies the absence of significant auto-correlation. The majority of auto-correlation values in the ACF plot are inside the confidence bands, indicating that the residuals do not exhibit any systematic patterns. Similarly, the PACF plot shows that partial auto-correlation falls within the confidence ranges, with no significant connection at any single lag. This analysis reveals that the residuals have white noise qualities, indicating that the ARIMA(\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) model accurately captured the time series\u0026apos; underlying structure. These diagnostics indicate that no further model improvements are required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable No:3 Ljung-Box Test (White Noise Test) Results\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest Statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3.6794\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDegrees of Freedom (df)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9607\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignificance Level (\u0026alpha;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNull Hypothesis (H₀)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResiduals are white noise (uncorrelated).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlternative Hypothesis (H₁)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResiduals are not white noise (correlated).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; compilation using time-series econometric techniques, referencing standard forecasting methodologies and established econometric models.\u003c/p\u003e\n\u003cp\u003eThe Box-Ljung test for the ARIMA (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) model shows that the residuals are compatible with white noise, as the p-value (0.9607) is significantly higher than the 5% significance level (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.05\\alpha\u0026thinsp;=\u0026thinsp;0.05\u0026alpha;\u0026thinsp;=\u0026thinsp;0.05). The test statistic (X2\u0026thinsp;=\u0026thinsp;3.6794X^2\u0026thinsp;=\u0026thinsp;3.6794X2\u0026thinsp;=\u0026thinsp;3.6794) is very small compared to the degrees of freedom (\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e), indicating no substantial auto-correlation in the residuals. This confirms that the ARIMA (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) model has successfully captured the structure of the time series, with no significant patterns left in the residuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable No: 4 Fitting the Model: ARIMA Model Summary\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARIMA Order\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR1 Coefficient (ar1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMA1 Coefficient (ma1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.9837\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMA2 Coefficient (ma2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9988\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard Error (ar1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard Error (ma1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard Error (ma2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSigma\u0026sup2; (Error Variance)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog Likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-84.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAIC (Akaike Information Criterion)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; compilation using time-series econometric techniques, referencing standard forecasting methodologies and established econometric models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable No: 5 Training Set Error Measures\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tabd\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eError Metric\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean Error (ME)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.2662\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoot Mean Square Error (RMSE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.7301\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean Absolute Error (MAE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.3892\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean Percentage Error (MPE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e226.2953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean Absolute Percentage Error (MAPE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e360.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean Absolute Scaled Error (MASE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4541\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutocorrelation of Residuals at Lag 1 (ACF1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; compilation using time-series econometric techniques, referencing standard forecasting methodologies and established econometric models.\u003c/p\u003e\n\u003cp\u003eThe ARIMA (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) model includes one Auto-Regressive (AR) term, one differencing term, and two Moving Average (MA) terms. The AR1 coefficient is weak (0.0221), while the MA1 (-1.9837) and MA2 (0.9988) coefficients are significant, indicating the importance of the moving average terms. The residual variance (\u0026sigma;2\u0026thinsp;=\u0026thinsp;260.5\\sigma^2\u0026thinsp;=\u0026thinsp;260.5\u0026sigma;2\u0026thinsp;=\u0026thinsp;260.5) shows the spread of errors, with a log-likelihood of -84.18 and an AIC of 176.36 for model evaluation.\u003c/p\u003e\n\u003cp\u003eTraining error metrics (RMSE\u0026thinsp;=\u0026thinsp;15.73, MAE\u0026thinsp;=\u0026thinsp;9.39, MAPE\u0026thinsp;=\u0026thinsp;360.03%) suggest forecasting challenges, particularly due to a high MAPE. However, low residual autocorrelation (ACF1 = -0.0948) indicates the model captures patterns effectively.\u003c/p\u003e\n\u003cp\u003eThe residual diagnostics confirm that the \u003cstrong\u003eARIMA\u003c/strong\u003e(\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) \u003cstrong\u003emodel\u003c/strong\u003e effectively captures the underlying structure of the time series, with no significant auto-correlation detected.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003e1. Residual Time Series Plot\u003c/strong\u003e: Residuals fluctuate around zero, indicating a good fit. However, large spikes in 2015 and 2020 suggest possible outliers or unaccounted events.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e\u003cstrong\u003e2. ACF Plot\u003c/strong\u003e: No significant auto-correlation is observed, confirming that residuals behave like white noise, meaning the model has properly captured time dependencies.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e\u003cstrong\u003e3. Histogram of Residuals\u003c/strong\u003e: Residuals are centered around zero, but slight skewness and deviations from normality are noted, possibly due to outliers.\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe ARIMA(\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) model appears to be a good fit, as residuals show no systematic patterns. However, outliers and slight deviations from normality may require further analysis or model refinements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable No: 6 Forecasted GSDP (₹ Lakh) for Karnataka (2025\u0026ndash;2029)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tabe\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePoint Forecast\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLo 80\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHi 80\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLo 95\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHi 95\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150,740,121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e140,593,415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e160,886,828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135,222,072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e166,258,170\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e161,780,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146,766,012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e176,794,016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138,818,080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e184,741,948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e172,535,552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e152,426,657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e192,644,447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e141,781,651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e203,289,453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e183,014,060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e157,546,175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208,481,945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e144,064,292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e221,963,829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e193,222,673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e162,138,551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e224,306,796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e145,683,610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240,761,736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; compilation using time-series econometric techniques, referencing standard forecasting methodologies and established econometric models.\u003c/p\u003e\n\u003cp\u003eEconomic forecasting is vital for strategic decision-making, helping policymakers, investors, and businesses navigate future trends. The projected Gross State Domestic Product (GSDP) of Karnataka for 2025\u0026ndash;2029 indicates steady economic growth, with key insights into forecasts, trends, and uncertainties.\u003c/p\u003e\n\u003cp\u003eThe figure presents the forecast Gross State Domestic Product (GSDP) of Karnataka from 2025 to 2029, along with confidence intervals to illustrate economic uncertainty.\u003c/p\u003e\n\u003ch2\u003ePoint Forecast (Blue Line):\u003c/h2\u003e\n\u003cp\u003eThe projected GSDP shows a steady upward trend \u003cstrong\u003efrom\u003c/strong\u003e ₹150.74 lakh crore in 2025 to ₹193.22 lakh crore in 2029, indicating continuous economic growth.\u003c/p\u003e\n\u003ch2\u003e80% Confidence Interval (Shaded Blue Region):\u003c/h2\u003e\n\u003cp\u003eThis represents a range where the actual GSDP is expected to fall 80% of the time. the interval gradually widens over time, indicating increasing uncertainty.\u003c/p\u003e\n\u003ch2\u003e95% Confidence Interval (Shaded Red Region):\u003c/h2\u003e\n\u003cp\u003eA wider range, covering \u003cstrong\u003egreater economic variability\u003c/strong\u003e. the uncertainty significantly increases towards \u003cstrong\u003e2029\u003c/strong\u003e, reflecting potential external economic shocks, policy changes, and structural adjustments.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe GSDP forecast for Karnataka (2025\u0026ndash;2029) indicates sustained economic growth, with an expected increase from ₹150.74 lakh crore in 2025 to ₹193.22 lakh crore in 2029. This projection reflects the state\u0026rsquo;s strong economic fundamentals and presents opportunities for investment, infrastructure development, and employment generation.\u003c/p\u003e \u003cp\u003eHowever, longer-term forecasts carry increasing uncertainty, as seen in the widening confidence intervals. Policymakers must closely monitor actual economic trends and be prepared to adjust strategies based on global economic conditions, sectoral shifts, and policy effectiveness.\u003c/p\u003e \u003cp\u003eTo sustain this growth trajectory, Karnataka should focus on innovation, infrastructure, workforce development, and economic diversification while managing external risks and fiscal stability. \u003cb\u003eA\u003c/b\u003e balanced approach to economic planning and policy execution will help maximize growth potential while ensuring resilience against economic shocks and uncertainties.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnand, M., \u0026amp; Bose, R. (2024). \u003cem\u003eIndia @ 100 and the significance of top six states\u003c/em\u003e (Working Paper No. 259). Madras School of Economics. \u003cstrong\u003eLink:\u003c/strong\u003e https://www.mse.ac.in/wp-content/uploads/2024/07/Working-Paper-259.pdf\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eDepartment of Commerce and Industries, Government of Karnataka. (2025). \u003cem\u003eKarnataka Industrial Policy 2025\u0026ndash;30\u003c/em\u003e. \u003cstrong\u003eLink:\u003c/strong\u003ehttps://investkarnataka.co.in/wpcontent/uploads/2025/02/IndustrialPolicy2025_PrintPagesSingle_.pdf\u003c/li\u003e\n\u003cli\u003eGovernment of Karnataka. (2022). \u003cem\u003eKarnataka economic survey 2021\u0026ndash;22\u003c/em\u003e. Department of Economics and Statistics. \u003cstrong\u003eLink:\u003c/strong\u003ehttps://des.karnataka.gov.in/storage/pdffiles/KARNATAKA%20ECONOMIC%20SURVEY%202021-22-M2_ENG_FINAL.pdf\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eGupta, A., \u0026amp; Jain, P. (2023). A statistical study of prediction of Indian GDP. \u003cem\u003eAsian Finance \u0026amp; Banking Studies, 11\u003c/em\u003e(3), 45\u0026ndash;58. \u003cstrong\u003eLink:\u003c/strong\u003ehttps://www.afjbs.com/uploads/paper/6ec4fa86795abc940998a40a29ad9dbe.pdf\u003c/li\u003e\n\u003cli\u003eKPSC Editorial Team. (2024). Karnataka\u0026rsquo;s economic growth: Mid-year review highlights. \u003cem\u003eKarnataka Public Service Commission\u003c/em\u003e. \u003cstrong\u003eLink:\u003c/strong\u003ehttps://www.nammakpsc.com/affairs/karnatakas-economic-growth-mid-year-review-highlights\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eKumar, S., \u0026amp; Dey, A. (2021). \u003cem\u003eQuarterly forecasting model for India\u0026rsquo;s economic growth: A Bayesian VAR approach\u003c/em\u003e (ADB Working Paper Series). Asian Development Bank. \u003cstrong\u003eLink:\u003c/strong\u003ehttps://www.adb.org/publications/quarterly-forecasting-model-economic-growth-india\u003c/li\u003e\n\u003cli\u003ePatel, R., \u0026amp; Kumari, S. (2024). Forecasting India\u0026apos;s GDP using ARIMA: Factors contributing to India\u0026apos;s growth. \u003cem\u003eInternational Journal of Applied Economic Forecasting\u003c/em\u003e. \u003cstrong\u003eLink:\u003c/strong\u003ehttps://www.researchgate.net/publication/384492968_Forecasting_India%27s_GDP_Using_\u003cbr\u003eARIMA_Factors_Contributing_To_India%27s_Growth\u003c/li\u003e\n\u003cli\u003eSharma, P., \u0026amp; Kulkarni, A. (2023). Regional disparities in economic growth of Karnataka. \u003cem\u003eInternational Journal for Multidisciplinary Research, 5\u003c/em\u003e(3), 34\u0026ndash;42. \u003cstrong\u003eLink:\u003c/strong\u003e https://www.ijfmr.com/papers/2023/5/7383.pdf\u003c/li\u003e\n\u003cli\u003eSinha, R. (2024, February 12). Karnataka\u0026apos;s GSDP growth at 10.2% outpaces national average: A testament to strong governance. \u003cem\u003eThe Times of India\u003c/em\u003e. \u003cstrong\u003eLink:\u003c/strong\u003ehttps://timesofindia.indiatimes.com/city/bengaluru/karnatakas-gsdp-growth-at-102-outpaces-national-average-a-testament-to-strong-governance/articleshow/114435689.cms\u003c/li\u003e\n\u003cli\u003eStaff Reporter. (2024, February 15). Karnataka\u0026apos;s GSDP projected to grow at 7.4%. \u003cem\u003eDaijiworld Media Network\u003c/em\u003e. \u003cstrong\u003eLink:\u003c/strong\u003ehttps://daijiworld.com/index.php/news/newsDisplay?newsID=1274492\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBox, G. E. P., \u0026amp; Jenkins, G. M. (1970).\u003c/strong\u003e \u003cem\u003eTime Series Analysis: Forecasting and Control\u003c/em\u003e. San Francisco: Holden-Day.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMakridakis, S., Wheelwright, S. C., \u0026amp; Hyndman, R. J. (1998).\u003c/strong\u003e \u003cem\u003eForecasting: Methods and Applications\u003c/em\u003e (3rd ed.). New York: Wiley.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHyndman, R. J., \u0026amp; Athanasopoulos, G. (2018).\u003c/strong\u003e \u003cem\u003eForecasting: Principles and Practice\u003c/em\u003e (2nd ed.). Melbourne, Australia: OTexts.en.wikipedia.org+1en.wikipedia.org+1\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eFranses, P. H. (1998).\u003c/strong\u003e \u003cem\u003eTime Series Models for Business and Economic Forecasting\u003c/em\u003e. Cambridge: Cambridge University Press.en.wikipedia.org\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eG\u0026oacute;mez, V., \u0026amp; Maravall, A. (1994).\u003c/strong\u003e Estimation, prediction, and interpolation for nonstationary series with the Kalman filter. \u003cem\u003eJournal of the American Statistical Association\u003c/em\u003e, 89(426), 611-624.en.wikipedia.org\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHyndman, R. J., \u0026amp; Khandakar, Y. (2008).\u003c/strong\u003e Automatic time series forecasting: The forecast package for R. \u003cem\u003eJournal of Statistical Software\u003c/em\u003e, 27(3), 1-22.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePankratz, A. (1983).\u003c/strong\u003e \u003cem\u003eForecasting with Univariate Box-Jenkins Models: Concepts and Cases\u003c/em\u003e. New York: Wiley.en.wikipedia.org\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eChatfield, C. (2000).\u003c/strong\u003e \u003cem\u003eTime-Series Forecasting\u003c/em\u003e. Boca Raton, FL: Chapman \u0026amp; Hall/CRC.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHamilton, J. D. (1994).\u003c/strong\u003e \u003cem\u003eTime Series Analysis\u003c/em\u003e. Princeton, NJ: Princeton University Press.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBrockwell, P. J., \u0026amp; Davis, R. A. (2002).\u003c/strong\u003e \u003cem\u003eIntroduction to Time Series and Forecasting\u003c/em\u003e (2nd ed.). New York: Springer.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTsay, R. S. (2005).\u003c/strong\u003e \u003cem\u003eAnalysis of Financial Time Series\u003c/em\u003e (2nd ed.). Hoboken, NJ: Wiley-Interscience.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Sri Sathya Sai University For Human Excellence","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Gross State Domestic Product, Time Series Forecasting, Economic Growth Trends, Adaptive Policy Making, Karnataka Economy 2025–2029","lastPublishedDoi":"10.21203/rs.3.rs-6749589/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6749589/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents an empirical forecast of Karnataka\u0026rsquo;s Gross State Domestic Product (GSDP) for the period 2025\u0026ndash;2029 using the ARIMA (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) time series model. Research fills a significant gap in the literature by providing a statistically robust and policy-relevant forecast for Karnataka, which is one of India's fastest-growing and diverse economy. The methodology uses 80% and 95% confidence intervals to measure economic uncertainty and show how external factors like global economic shifts, inflation, and policy reforms can affect forecast variability. The study offers policy recommendations for infrastructure, sectoral diversification, skill development, and budgetary resilience. and also, the study emphasizes the need for data-driven policymaking to promote inclusive economic growth and long-term stability in Karnataka.\u003c/p\u003e","manuscriptTitle":"Predicting Karnataka’s GSDP Trajectory: Data-Driven Trends and Strategic Policy Directions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-28 08:20:11","doi":"10.21203/rs.3.rs-6749589/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":"ac3d2fdf-dec2-42bc-ac25-968ea3169abc","owner":[],"postedDate":"May 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49045603,"name":"Other Economics"}],"tags":[],"updatedAt":"2025-05-28T08:20:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-28 08:20:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6749589","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6749589","identity":"rs-6749589","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}