Rethinking Global Macroeconomic Causality: A Structural VAR Model Based on U.S. Evidence

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P M Savadatti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6720708/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 By presenting a new Structural Vector Autoregression (SVAR) Model that questions the traditional ordering of interest rates, inflation, and GDP growth, this study examines macroeconomic causality. Interest rates are frequently portrayed in traditional macroeconomic models as proactive policy instruments that affect prices and output. However, the present research highlights the reactive nature of modern monetary policy by putting forth an alternative causal structure that draws on post-2008 and post-pandemic global economic disruptions. In this structure, real and supply-side shocks drive GDP growth and inflation simultaneously, while interest rates react later. Utilizing annual data from 1961 to 2025 in the United States, the study uses rigorous time-series techniques such as forecast error variance decomposition (FEVD), impulse response functions (IRFs), VAR diagnostics, and Augmented Dickey-Fuller (ADF) tests. The findings show that inflation dominates monetary policy response, GDP growth shows considerable self-dependence, and interest rates have a limited and delayed short-term impact on macroeconomic variables. While the empirical foundation is U.S.-specific, the findings have significant global implications. In an era of inflation persistence, geopolitical shocks, and central banks operating near the lower bound, this revised SVAR Model offers a globally adaptable approach for understanding dynamic macroeconomic interactions. The study contributes to the evolving literature on monetary transmission by reordering causality in a way that better reflects contemporary macroeconomic realities. Other Economics Structural Vector Autoregression (SVAR) Macroeconomic Causality GDP Growth Inflation Dynamics Interest Rate Policy Monetary Transmission Mechanism Impulse Response Function (IRF) Forecast Error Variance Decomposition (FEVD) Time Series Econometrics Reactive Monetary Policy Introduction The complex relationship among GDP growth, inflation, and interest rates continues to be a fundamental focus in the analysis of macroeconomic policy. Traditional macroeconomic theories, such as the Phillips Curve (Phillips, 1958) and the Monetary Policy Transmission Mechanism (Bernanke & Gertler, 1995), postulate that interest rate policies by central banks influence inflation and output growth, typically assuming an immediate reaction of financial variables to policy adjustments. However, recent structural shifts in the global economy including post-2008 unconventional monetary policies, the COVID-19 pandemic, and supply chain disruptions suggest that these relationships have changed (Borio & Disyatat, 2015; Forbes et al., 2021; IMF, 2020). In this research, Researcher reexamine the relationship among GDP growth, inflation, and interest rates within the framework of the United States economy, utilizing a Structural Vector Autoregression (SVAR) Model. And a researcher proposed a revised causal ordering based on observed economic patterns in the last two decades. Specifically, study hypothesize that output (GDP growth) and inflation are immediately affected by domestic and global shocks, whereas interest rate adjustments by the Federal Reserve follow these developments with a lag, reflecting the central bank’s reactive stance (Clarida, Gali, & Gertler, 2000). This approach not only aligns with recent literature highlighting the delayed transmission of monetary policy (Borio & Disyatat, 2015) but also incorporates the increasing influence of supply-side factors and external shocks on inflation (Forbes et al., 2021). Through this analysis, researcher aimed to provide a more accurate interpretation of macroeconomic policy effectiveness in a dynamically changing economic environment. Motivation for the Study The relationship among GDP growth, inflation, and interest rates is fundamental to the analysis of macroeconomics and the development of policy. Traditionally, economic models assume that interest rates controlled by central banks serve as the primary tool for influencing output and inflation. However, recent global disruptions, including the 2008 financial crisis, the COVID-19 pandemic, and ongoing supply chain bottlenecks, have altered the landscape of macroeconomic interactions. These structural shifts raise critical questions about the validity of the conventional causal ordering used in macroeconomic modeling. Empirical observations from the past two decades show that monetary policy often reacts to inflation and output trends rather than proactively shaping them. Moreover, supply-side factors and external shocks now play a dominant role in shaping inflation dynamics, reducing the immediate effectiveness of interest rate adjustments. This evolving economic reality demands a re-examination of traditional models. By employing a Structural VAR Model, this research aims to present new empirical evidence indicating that GDP growth and inflation respond to structural shocks simultaneously, whereas interest rates adjust with a delay, reflecting a reactive approach to policy. The motivation for this study lies in bridging the gap between traditional macroeconomic theory and current empirical observations. It aims to enhance our understanding of macroeconomic policy effectiveness in today’s uncertain and rapidly changing global economic environment, thereby contributing both to academic literature and policy-making discourse. Review of Literature Comparative Review of Macroeconomic Theories and Their Relevance to the Study Understanding the dynamic interactions between GDP growth, inflation, and interest rates has been a central concern in macroeconomic theory. The present study revisits this relationship through a revised Structural VAR (SVAR) framework, drawing upon and challenging the assumptions embedded in classical, Keynesian, monetarist, New Keynesian, and Post-Keyesian paradigms. Below is a synthesis of how these schools of thought interpret the mechanisms of macroeconomic adjustment and how these views frame the motivation behind rethinking causal ordering. Classical versus Keynesian Approaches Classical economics postulates that markets possess self-correcting mechanisms, suggesting that any deviations from full employment are only temporary. In this context, inflation is viewed solely as a monetary issue, with interest rates adjusting to balance savings and investment, thereby negating any long-term trade-off between inflation and output. As a result, interest rate shocks are treated as exogenous and immediate, aligning seamlessly with a Cholesky-ordered SVAR structure where monetary policy is the leading factor. In contrast, Keynesian economics argues that economies can experience extended periods of insufficient demand. It recognizes that prices and wages tend to be rigid, and that interest rates, influenced by central banks, have a significant impact on investment and output in the short term. The Keynesian model endorses a more endogenous perspective on interest rates and emphasizes the importance of aggregate demand in driving GDP growth. This aligns with the hypothesis of this study, which posits that GDP and inflation react to real sector shocks, while interest rates tend to respond with a delay. Keynesian versus Monetarist Theories Both economic schools acknowledge the short-term effectiveness of economic policy but diverge in their preferred tools. Monetarists, particularly Milton Friedman, advocate for the regulation of money supply growth, asserting that inflation is fundamentally a monetary phenomenon. They also caution against the use of activist fiscal policies. However, the empirical evidence from our study reflects a Keynesian viewpoint, indicating that in recent years, central banks have frequently opted for delayed interest rate adjustments rather than proactively leading economic cycles. This observed delay, particularly evident in the monetary policy following the 2008 financial crisis and the stimulus measures during the COVID-19 pandemic, prompts us to reconsider the traditional SVAR ordering. Monetarist models, which assume that inflation responds swiftly to changes in the money supply, find it challenging to account for the sustained low inflation observed during prolonged periods of monetary expansion a conundrum that our SVAR analysis seeks to resolve. Monetarist vs. New Keynesian Synthesis New Keynesians incorporate rational expectations and micro foundations, enhancing the analytical rigor of Keynesian insights. They transition their emphasis from targeting the money supply to implementing interest rate guidelines, like the Taylor Rule, which connects interest rate decisions to inflation levels and output discrepancies. This transition reflects contemporary monetary policy frameworks where central banks aim to stabilize inflation expectations rather than control money supply. Our study extends this logic but questions the assumption of policy leadership implicit in these models. Using SVAR, Researcher test whether interest rates really lead and influence GDP growth and inflation, or if they lag as a reactive tool a view supported by the empirical evidence of delayed monetary responses and nonlinear inflation dynamics. New Keynesian vs. Post-Keynesian Perspectives Post-Keynesians critique the mainstream focus on equilibrium modeling and rational expectations. They highlight the importance of structural uncertainty, financial instability, and the demand-driven nature of long-term output. Their analysis centers on factors such as cost-push inflation, conflicts in income distribution, and the endogeneity of money and interest rates. This alternative perspective closely aligns with the theoretical revisions presented in our study. We contend that supply shocks, fluctuations in demand, and institutional delays require a reconfiguration of causal relationships within SVAR models. Rather than viewing interest rates as exogenous shocks, our model treats them as endogenously influenced by prior changes in GDP and inflation, which supports the Post-Keynesian view of a demand-oriented and institutionally grounded macroeconomic framework. Incorporating Empirical Models: IS-LM, AD-AS, Phillips Curve, and Taylor Rule While macroeconomic theory lays the groundwork with its fundamental assumptions, empirical models are essential for simulating the complexities of real-world dynamics: IS-LM Model: Demonstrates the relationship between interest rates and economic output, under the assumption of constant prices in the short term. Our research builds on this by examining if interest rates actually respond after output changes, not before. AD-AS Model : Reflects both demand and supply factors in determining price and output levels. The SVAR model adopted here reflects such dual shocks, particularly in accounting for inflation's response to supply bottlenecks. Phillips Curve : Traditionally portrayed a trade-off between inflation and unemployment. Our findings support recent critiques that this relationship has weakened due to global supply chains and technology, necessitating revised modeling strategies. Taylor Rule : Offers a policy benchmark linking interest rates to inflation and output gaps. Our SVAR estimation, however, calls into question the underlying assumption that central banks operate in a contemporaneous or proactive manner. This research situates itself at the intersection of macroeconomic theory and econometric innovation. By revisiting causal ordering in a structural framework, the study not only builds on but critically evaluates the assumptions of traditional models. While Classical and Monetarist views offer long-run insights, they often miss short-run policy lags and structural shocks. Keynesian and New Keynesian frameworks accommodate these realities but still presume a leading role for interest rates in steering the economy. Post-Keynesian critique and real-world developments such as delayed Fed responses, persistent inflation, and global shocks demand a new perspective. This study contributes by reordering causality, placing GDP growth and inflation ahead of interest rate adjustments, which better reflects contemporary macroeconomic behavior and enhances the explanatory power of SVAR modeling in policy research. Reviewed Research Papers Brandão‑Marques et al. (2024) – Negative Interest Rate Policies: A Survey This comprehensive review analyzes the theoretical rationale, empirical impacts, and global experiences with negative interest rate policies (NIRPs). The authors explore how NIRPs, adopted by central banks in the Eurozone, Japan, and others, affect output, inflation, and financial stability. The study identifies transmission mechanisms and limitations, particularly the diminished effectiveness in low-rate environments. It reinforces the idea that the effects of monetary policy can differ depending on the context, thereby questioning the straightforward relationship between interest rates and inflation. this is particularly relevant to our SVAR-based rethinking of policy lags and effects. Aoki & Ueda (2025) – This study explores Japan's implementation of unconventional monetary policy measures, including quantitative easing (QE), yield curve control (YCC), and negative interest rates, aimed at addressing ongoing low inflation and stagnant GDP growth. The authors assess the effectiveness of these strategies through empirical analyses utilizing VAR, SVAR, and DSGE models. They conclude that while monetary easing has led to modest improvements in GDP growth, its influence on inflation expectations has been limited. Their results support the notion that in economies facing structural constraints, inflation does not always respond to interest rate fluctuations. Nguyen (2020) Nguyen performs a meta-analysis encompassing over 45 studies to investigate the effects of monetary tightening on output in emerging and developing economies (EMDEs). The findings consistently indicate that contractionary monetary policy results in significant reductions in GDP growth, although the extent of these effects varies depending on institutional quality and economic openness. The research highlights the variability in policy impacts across different economies, affirming the necessity of employing differentiated structural models such as SVARs. This study supports the argument that both global and domestic shocks should be meticulously disaggregated to accurately evaluate causal relationships. Balima et al. (2020) This Study employs meta-regression analysis on over 8,000 estimates to evaluate the credibility and effectiveness of inflation targeting (IT). The research reveals that, even after adjusting for selection biases, IT regimes are linked to lower inflation rates and marginally improved growth performance. This finding is noteworthy as it implies that stability in inflation may precede GDP responses, challenging the conventional output-to-inflation relationship. Additionally, the paper indirectly supports models like our that reconsider lag structures and the responsiveness of policy under varying macroeconomic conditions. Ono (2021) – Monetary Policy in Russia: A Factor-Augmented VAR Approach Ono employs a factor-augmented VAR (FAVAR) model to investigate the macroeconomic reactions of Russia to monetary shocks. The research reveals that unexpected increases in interest rates result in heightened inflation, primarily influenced by exchange rate pass-through and imported inflation. This surprising outcome challenges conventional macroeconomic theories and underscores the necessity to reconsider the traditional causal sequences in VAR/SVAR models. It lends empirical support to the notion that GDP and inflation respond initially, followed by adjustments in interest rates. Gregor, Melecký & Melecký (2021) – This review synthesizes results from 54 empirical studies that explore the transmission of central bank policy rates to lending and deposit rates across various countries. The meta-analysis indicates that the pass-through effect is frequently incomplete and varies considerably depending on market structure, inflation targeting strategies, and the credibility of monetary policy. The results imply that the anticipated transmission of interest rate policy to macroeconomic indicators such as GDP and inflation is neither consistent nor immediate, reinforcing the concept of a delayed policy response as suggested in our SVAR model framework. Pappas & Boukas (2025) –This study conducts an empirical analysis of the combined effects of inflation and inflation volatility on GDP growth within EU nations. The findings indicate that both elevated inflation and fluctuating inflation levels significantly hinder long-term growth. The methodology includes panel data econometrics and robust cross-country regressions. The paper’s conclusion aligns with our research that inflation’s impact on output is often non-linear and context-dependent, thereby reinforcing the relevance of flexible causal orderings in structural modeling frameworks. Bennett & Owyang (2022) – This review investigates why most institutional and private inflation forecasts particularly those in the U.S. underpredicted the inflation surges post-COVID. It reveals systematic forecast errors driven by model misspecification, especially underestimating the effects of supply shocks and stimulus-driven demand surges. The authors argue for updated modeling strategies that account for asymmetries and delayed effects in the inflation-output-interest rate nexus. Haschka (2024) – Haschka analyzes the declining explanatory power of the Phillips Curve using U.S. macro data. The study shows that since the 1980s, and especially post-2008, the inflation-unemployment trade-off has weakened. Technological changes, labor market slack, and better-anchored inflation expectations are identified as causes. The review employs various structural models, including New Keynesian DSGEs and SVARs. Binder & Kamdar (2022) – This study examines historical episodes where inflation expectations diverged significantly from realized inflation e.g., the 1970s Great Inflation vs. the post-COVID inflation surge. The authors conclude that well-anchored expectations reduce the cost of monetary tightening on growth. Their analysis draws from multiple time-series and SVAR models. Chen & Kim (2024) This study explores the nonlinear relationship between inflation and GDP growth in China using a threshold SVAR model. The authors find that when inflation is below 2%, it has a mildly positive effect on economic growth due to increased price signals and moderate demand expansion. However, inflation above this threshold leads to a sharp decline in GDP growth due to rising costs and uncertainty. This nuanced view underscores the importance of considering regime-switching behavior in inflation’s macroeconomic effects. The study strengthens our argument that inflation should not be treated as a linear function of interest rates or GDP, but rather as a dynamic response with feedback effects. LaBelle & Santacreu (2022) This study reviews the inflationary impact of pandemic-induced global supply chain disruptions. It estimates that more than half of the U.S. core inflation increase in 2021–22 can be attributed to supply-side bottlenecks rather than demand-side overheating. The study uses decomposition techniques and structural models to isolate the drivers of inflation. The findings challenge traditional views of monetary transmission, suggesting that interest rate hikes are ineffective against supply-driven inflation. This supports our revised SVAR causal ordering, where inflation emerges from real-sector shocks before interest rate responses. Ahmed et al. (2023) This UK-focused study employs SVAR modeling to examine how oil price shocks influence inflation and GDP growth. The findings indicate that oil prices pass through rapidly to inflation, while GDP responds negatively with a delay. Interestingly, monetary tightening is shown to reduce output in the short run without immediately dampening inflation. This time lag between interest rate decisions and macroeconomic impact reinforces our hypothesis that central banks often act in response to existing inflation and growth trends rather than preemptively. Olaoye et al. (2024) The authors conduct a panel analysis of 44 Sub-Saharan African countries to investigate the primary causes of inflation. Their findings reveal that inflation is largely driven by fiscal imbalances specifically rising public debt and budget deficits rather than money supply or direct interest rate changes. The study challenges monetary-dominant narratives and shows that inflation often emerges from the real and fiscal sectors. This reinforces the logic in our SVAR model that inflation and GDP respond to structural shocks first, with interest rates adjusting later based on fiscal conditions. Cieslak & Pflueger (2023) This review connects macroeconomic inflation dynamics with financial market performance, focusing on asset pricing under different inflation regimes. It shows that unexpected inflation typically depresses both equity and bond returns due to uncertainty and eroded real yields. However, in certain scenarios, credible monetary tightening can stabilize expectations and even support asset prices. The authors emphasize the importance of inflation expectations and central bank credibility. Their findings provide indirect but powerful support for treating inflation as a pivotal channel influencing monetary response in our SVAR Model. Dräger & Lamla (2024) This Study reviews contemporary research regarding how consumers develop expectations concerning inflation, interest rates, and future GDP. The authors contend that consumer expectations frequently diverge from expert predictions and are shaped by recent personal experiences, media coverage, and noticeable price fluctuations (such as those in fuel or food). These behavioral inconsistencies pose challenges for effective monetary policy, as the results of such policies depend not only on changes in interest rates but also on public perception of those changes. This finding reinforces our revised model structure by indicating that inflation expectations, and consequently inflation itself, are only loosely tied to central bank actions in the short term. Akalpler (2025) Akalpler’s research investigates Turkey’s unconventional monetary policy, which emphasized GDP growth through low interest rates despite escalating inflation. The results indicate short-term GDP improvements but a significant rise in inflation and currency devaluation. This policy approach resulted in long-term macroeconomic instability, demonstrating the limitations of demand-driven growth amid high inflationary pressures. The author points out that changes in interest rates were reactive to economic developments rather than proactive. This observation aligns perfectly with our updated SVAR framework, where shocks to output and inflation occur prior to adjustments in policy rates. Bayir & Orak (2024) This empirical research analyzes inflation dynamics in five major emerging economies: Brazil, Russia, India, China, and Turkey. Utilizing panel VAR methodologies, the authors discover that fluctuations in exchange rates and output growth exert a more immediate and significant impact on inflation compared to movements in interest rates. The findings affirm that in emerging markets, monetary policy instruments are frequently limited by global shocks and capital flows. These results bolster our model's hypothesis that inflation is primarily driven by real economic shocks, with interest rate adjustments occurring later in the response cycle. Bauer & Rudebusch (2020) This review examines the persistent decline in natural interest rates, referred to as “r-star,” in developed nations. The authors identify several contributing factors to this trend, including demographic shifts, reduced productivity, and a heightened global appetite for safe assets. As real interest rates continue to decrease, central banks are confronted with the zero-lower-bound issue, which restricts their capacity to combat inflation or foster growth through traditional interest rate adjustments. The findings strongly endorse the notion that monetary policy has shifted towards a more reactive stance rather than a proactive one, thereby validating the use of a SVAR framework in which interest rates respond with a delay. Ha, Kose & Ohnsorge (2022) This comprehensive review analyzes the evolution of inflation dynamics in emerging markets and developing economies (EMDEs) from the high-inflation era of the 1970s to the relatively stable period of the 2010s, culminating in the inflation surge following the COVID-19 pandemic. The authors point out that structural reforms, credible inflation targeting, and improved fiscal discipline have enabled many EMDEs to manage inflation effectively until global supply shocks reintroduced instability. Notably, the paper underscores that recent inflationary trends are increasingly driven by supply factors and have a global dimension. This observation reinforces the foundational assumption of our SVAR model inflation shocks are now more independent and less directly linked to domestic interest rate policies. Research Gap Although there is a significant amount of empirical and theoretical research regarding the dynamic relationships among GDP growth, inflation, and interest rates, a notable gap remains in comprehending how the causal relationships among these variables have changed due to structural transformations in the global economy. Traditional macroeconomic theories ranging from Classical and Monetarist views to New Keynesian frameworks largely assume a policy-led approach where central bank interest rates precede and influence inflation and output, forming the conventional ordering used in Structural VAR (SVAR) modeling (Brandão‑Marques et al., 2024; Balima et al., 2020; Nguyen, 2020). However, emerging evidence challenges these assumptions. Recent studies show that interest rate responses are often delayed or ineffective in influencing inflation, especially when inflation is driven by exogenous supply shocks or global fiscal spillovers (LaBelle & Santacreu, 2022; Bennett & Owyang, 2022; Ha, Kose & Ohnsorge, 2022). For instance, Aoki & Ueda (2025) demonstrate that Japan's unconventional monetary policy tools yielded limited effects on inflation expectations, while Ahmed et al. (2023) and Bayir & Orak (2024) show that inflation and GDP growth often react first to real-sector disturbances such as oil price shocks or exchange rate fluctuations, followed later by monetary policy adjustments. Furthermore, empirical studies across diverse economic settings ranging from developed economies like the U.S. and EU to emerging markets such as Turkey, India, and Sub-Saharan Africa confirm the asymmetry and delay in monetary transmission mechanisms (Ono, 2021; Olaoye et al., 2024; Akalpler, 2025). These findings reveal that real economic shocks now increasingly drive inflation and output, with interest rates acting more as reactive stabilization tools than proactive levers (Cieslak & Pflueger, 2023; Dräger & Lamla, 2024). Despite these insights, most SVAR-based studies continue to adopt a traditional causal structure where interest rate shocks lead the system a structure that may no longer reflect contemporary macroeconomic dynamics. While some works (e.g., Chen & Kim, 2024) begin to address nonlinear and threshold effects, few studies have explicitly restructured SVAR models to reverse the standard ordering of interest rate, inflation, and GDP growth based on post-2008 realities and post-pandemic monetary policy behavior. This creates a two-fold research gap Theoretical Gap : A disconnect between evolving macroeconomic realities (e.g., inflation persistence, supply shocks, delayed policy reactions) and the conventional theoretical foundations of SVAR ordering grounded in outdated assumptions. Empirical Gap : A lack of SVAR-based studies especially in the context of the United States that systematically test alternative causal orderings, such as placing GDP growth and inflation as contemporaneously endogenous variables and treating interest rate adjustments as lagged, policy-reactive responses. Therefore, this study aims to bridge this gap by developing and empirically validating a revised SVAR framework that rethinks the causal sequencing of GDP growth, inflation, and interest rates. In doing so, it contributes not only to the econometric modeling literature but also to a more accurate understanding of macroeconomic policy effectiveness in today's volatile global environment. Objectives To empirically examine the causal relationship between GDP growth, inflation, and interest rates in the United States using a SVAR. To challenge and re-evaluate causal ordering used in SVAR models, by testing a revised sequence wherein GDP growth and inflation precede interest rate adjustments. Model and Theory Justification 1. Justification for Using Structural VAR (SVAR) The Structural Vector Autoregression (SVAR) framework has been selected for this research because of its strong ability to identify dynamic interrelationships among macroeconomic variables, while also facilitating the theoretical identification of structural shocks. In contrast to conventional VAR models, which lack a theoretical foundation and only reveal reduced-form relationships, the SVAR model applies economically relevant constraints derived from theory to distinguish the effects of both policy and non-policy shocks. Given the objective of re-examining the causal order among GDP growth (Yₜ), inflation (πₜ), and interest rates (iₜ), SVAR provides an ideal platform because it enables: Identification of contemporaneous structural innovations. Mapping of impulse responses to specific economic shocks. Testing of alternative causal orderings via imposed restrictions in the A and B matrices. This study adopts a short-run (contemporaneous) restriction-based SVAR using an A-model formulation, where the structure is justified by evolving macroeconomic realities, including delayed monetary policy response and real-sector-driven inflation. Justification for the Revised Causal Ordering: Yₜ → πₜ → iₜ Conventional SVAR models in macroeconomics typically assume that interest rate shocks precede and influence inflation and output , which mirrors the monetarist and New Keynesian belief that central banks proactively steer the economy. This study contests that Assumptions by introducing an updated causal framework in which GDP growth (Yₜ) reacts contemporaneously to domestic and global demand/supply shocks. Inflation (πₜ) adjusts immediately to both output conditions and supply chain factors. Interest rates (iₜ) respond with a lag , reflecting the reactive nature of monetary policy in recent decades. This revised ordering is theoretically supported by: Post-Keynesian economics , which views interest rates as an endogenous policy instrument. Empirical findings showing that monetary policy responses often lag behind inflationary and growth trends (e.g., Aoki & Ueda, 2025; LaBelle & Santacreu, 2022). The fading effectiveness of interest rates during zero lower bound environments , further justifying their placement as lagged responses in the system. Research Methodology This study employs a quantitative, time-series econometric approach using a Structural Vector Autoregression (SVAR) Model. The central objective is to empirically test a revised causal ordering between GDP growth (Yₜ), inflation (πₜ), and interest rate (iₜ) in the context of the United States economy. The methodology integrates both theoretical model-building and empirical validation to re-express macroeconomic relationships in light of contemporary policy lags, structural shocks, and post-crisis dynamics. Unlike conventional SVARs that place interest rates as the leading variable, this research introduces a different identification framework in which output and inflation react simultaneously to structural shocks, while interest rates adjust with a delay. Table No 1: Data Source and Description Parameter Details Time Period 1961 to 2025 Frequency Annual Country United States Variables Used GDP Growth (Yₜ) Real GDP growth rate (Annual %) Inflation (πₜ) Consumer Price Index (CPI) – Annual % change Interest Rate (iₜ) Federal Funds Rate (%) Source: Compiled by author Data Preprocessing and Stationarity Testing Before SVAR estimation, all three series are tested for stationarity using the Augmented Dickey-Fuller (ADF) test. Table No 2: ADF test results Variable ADF Statistic p-value Stationarity Decision GDP Growth (Yₜ) -4.7158 0.01 Stationary at Level Inflation (πₜ) -2.6294 0.3198 Non-stationary → Differenced Interest Rate (iₜ) -2.5921 0.335 Non-stationary → Differenced Source: Compiled by author Model Specification: SVAR Model The reduced-form VAR model is expressed as: [Yₜ, πₜ, iₜ] = C + A₁ [Yₜ₋₁, πₜ₋₁, iₜ₋₁] + εₜ Where: - C: Vector of intercepts - A₁: Matrix of lagged coefficients - εₜ ~ N(0, Σ): Vector of reduced-form errors - Structural shocks uₜ are retrieved using the transformation εₜ = B uₜ Identification Strategy (Short-Run Restrictions) The study adopts short-run (recursive) restrictions with the following structural ordering: Yₜ → πₜ → iₜ This implies: - GDP growth (Yₜ) responds only to its own structural shocks contemporaneously. - Inflation (πₜ) responds to both GDP and its own shocks. - Interest Rate (iₜ) responds with a lag and is influenced by past output and inflation conditions. The B matrix for structural identification is defined as an identity matrix: [1 0 0] B = [0 1 0] [0 0 1] This simplified structure assumes no contemporaneous cross-effects except from structural innovations within each variable. Model Estimation and Diagnostics The SVAR model is estimated through maximum likelihood estimation, utilizing a lag length of 1 as determined by information criteria such as AIC and BIC. Diagnostic Tests Conducted: - Stability Test: All characteristic roots lie inside the unit circle → Model is stable. - Serial Correlation Test (Portmanteau): No autocorrelation detected (p = 0.9182). - Heteroskedasticity Test (ARCH): No heteroskedasticity in residuals (p = 0.3912). Analytical Tools and Procedures The following tools are used to evaluate dynamic relationships: - Impulse Response Functions (IRFs): This method evaluates how each variable reacts over time to a structural shock. - Forecast Error Variance Decomposition (FEVD): This technique examines the extent to which forecast errors in each variable can be traced back to specific structural shocks. - Residual Correlation Matrix: To ensure residual independence. - Structural Impact Matrix (B-Matrix): To implement identification restrictions. All analyses are conducted utilizing R Studio, Justification of Methodological Choice SVAR is superior to VAR in identifying causal mechanisms in a theoretically grounded manner. The revised structural ordering tested in this model reflects contemporary macroeconomic realities lagged policy reactions, inflation inertia, and real-sector dominance. Using Time-Series techniques with rigorous pre-model testing ensures validity and robustness of inferences. Results and Discussions Table No 3: Descriptive summary table: Indicator Name Minimum (%) Maximum (%) Mean (%) Standard Deviation (%) GDP Growth (%) -2.60 7.12 3.00 2.06 Inflation (%) -0.03 11.80 3.84 2.76 Interest Rate (Federal Reserve System USA) 0.08 16.40 4.80 3.57 Source: Compiled by author The above table presents a descriptive analysis of key macroeconomic indicators of the United States, specifically focusing on GDP growth, inflation rates, and the Federal Reserve’s interest rates over the observed period. The findings reveal that GDP growth rates fluctuated between a minimum of -2.60%, indicating recessionary periods, and a maximum of 7.12%. The average growth rate was noted at 3.00%, with a standard deviation of 2.06%, suggesting a moderate degree of variability in economic growth. Inflation rates exhibited greater volatility, fluctuating between -0.03% and 11.80%, with a mean of 3.84% and a standard deviation of 2.76%. The Federal Reserve’s interest rates demonstrated significant variability, ranging from a low of 0.08% to a peak of 16.40%, averaging 4.80% with a standard deviation of 3.57%. These descriptive statistics highlight the dynamic nature of the U.S. economy, marked by fluctuations in growth, price stability, and monetary policy Stationary Check Table No 4: Consolidated ADF Test Results Variable Dickey-Fuller Test Statistic Lag Order p-value Stationarity Decision GDP (gdp_ts) -4.7158 3 0.01 Stationary (Reject H₀ at 1% level) Inflation (inflation_ts) -2.6294 3 0.3198 Non-Stationary (Fail to reject H₀) Interest Rate (interest_ts) -2.5921 3 0.335 Non-Stationary (Fail to reject H₀) Source: Compiled by author the above table Augmented Dickey-Fuller (ADF) test on three core U.S. macroeconomic time series GDP growth, inflation, and interest rates to assess their stationarity. The results indicate that GDP growth is stationary at level, while both inflation and interest rate series exhibit non-stationarity and require first differencing. These findings are critical for ensuring the reliability of time series models such as VAR and SVAR, as incorporating non-stationary variables without transformation may lead to spurious results. The analysis confirms the necessity of pre-testing for unit roots in empirical macroeconomic research. Consolidated Summary and Interpretation of ADF test and VAR Estimation Table No 5: Stationarity Diagnosis Using ADF Test Variable ADF Test Statistic p-value Stationarity at Level Action Taken GDP Growth (gdp_ts) -4.7158 0.01 Stationary Used as is (difference stationary) Inflation (inflation_ts) -2.6294 0.3198 Non-Stationary Differenced once Interest Rate (interest_ts) -2.5921 0.335 Non-Stationary Differenced once Source: Compiled by author The above table presents unit root diagnostics using the Augmented Dickey-Fuller (ADF) test for three key macroeconomic variables: GDP growth, inflation, and interest rates. The test reveals that GDP growth is stationary at level, indicating that it can be used in its original form for time series modeling. In contrast, inflation and interest rate series are non-stationary at level with p-values above conventional significance thresholds. To ensure valid inference and model stability, these two series were differenced once to achieve stationarity. These transformations form the foundational step for further econometric modeling using VAR and SVAR frameworks. Table No 6: Equation-Based Estimates of the VAR Model: Dynamic Interactions Between GDP Growth, Inflation, and Interest Rates (Differenced Series) Dependent Variable Independent Variable Coefficient p-value Significance GDP_Growth_diff GDP_Growth_diff.l1 -0.54363 7.99e-05 *** GDP_Growth_diff Inflation_diff.l1 -0.76082 0.000101 *** GDP_Growth_diff InterestRate_diff.l1 -0.28266 0.137799 NS Inflation_diff GDP_Growth_diff.l1 0.05644 0.50172 NS Inflation_diff Inflation_diff.l1 0.34762 0.00486 ** Inflation_diff InterestRate_diff.l1 -0.32591 0.00994 ** InterestRate_diff GDP_Growth_diff.l1 0.16412 0.0755 . InterestRate_diff Inflation_diff.l1 0.64476 5.52e-06 *** InterestRate_diff InterestRate_diff.l1 0.23314 0.0846 . Source: Compiled by author This table displays the findings from a Vector Autoregression (VAR) model that was estimated using first-differenced data on GDP growth, inflation, and interest rates. The equation for GDP growth reveals substantial negative impacts from its own lag and from lagged inflation, suggesting contractionary effects. Inflation is notably affected by its historical values and is inversely related to interest rates, indicating the influence of monetary policy. The equation for interest rates shows a significant response to previous inflation levels, aligning with a reactive approach to monetary policy. Notably, GDP growth does not have a significant effect on either inflation or interest rates. In summary, inflation stands out as a key macroeconomic variable that influences both economic output and the behavior of monetary policy. Table No 7 : Residual Correlation Matrix GDP_Growth_diff Inflation_diff InterestRate_diff GDP_Growth_diff 1.0000 0.0131 -0.4650 Inflation_diff 0.0131 1.0000 0.2561 InterestRate_diff -0.4650 0.2561 1.0000 Source: Compiled by author In the above table residual correlation matrix offers valuable information regarding the linear associations between the error terms of the VAR equations. A notable finding is the negative correlation (-0.4650) between GDP growth and interest rate residuals, indicating that shocks to interest rates tend to move inversely with output. A moderate positive correlation (0.2561) exists between inflation and interest rate residuals, suggesting some shared influences or feedback mechanisms between price levels and monetary policy. The minimal correlation (0.0131) between GDP growth and inflation residuals implies limited contemporaneous association between these variables after accounting for lagged dynamics. This research utilizes ADF tests and VAR estimation to examine the relationships among GDP growth, inflation, and interest rates through differenced series. The ADF results indicate that GDP growth is stationary, while inflation and interest rate series require differencing. The VAR analysis reveals that GDP growth is significantly and negatively impacted by its own lag and inflation, whereas interest rates do not exhibit a significant effect. Inflation shows persistence and is adversely influenced by interest rates. Additionally, interest rates are most responsive to changes in inflation, indicating a policy reaction function. The correlations of residuals suggest a negative relationship between GDP growth and interest rates, alongside a moderate positive association between inflation and interest rates. These interactions highlight the pivotal role of inflation in influencing macroeconomic outcomes. The above impulse response analysis investigates the dynamic effects of a one-standard deviation shock to interest rates on GDP growth, inflation, and interest rate itself over a 10-period horizon. The findings reveal that interest rate hikes initially suppress GDP growth, which temporarily rebounds but ultimately stabilizes, with no significant long-term impact. Inflation also responds negatively in the short term, affirming the monetary policy's effectiveness in curbing inflationary pressures, though the effect fades over time. Interest rates exhibit a strong immediate self-response, followed by gradual mean reversion. Overall, the analysis underscores the short-run contractionary effects of interest rate shocks and their diminishing influence over time. Diagnostic Checking of VAR Model for USA GDP Growth, Inflation Rate, and Interest Rate (1961–2025) Table No 8: Diagnostic Results of VAR Model Diagnostic Test Test Statistic Degrees of Freedom (df) p-value Conclusion 1. Stability (Roots of Characteristic Polynomial) Roots: 0.4981, 0.4981, 0.4100 — — All roots < 1 → VAR is stable 2. Serial Correlation (Portmanteau Test) 112.79 135 0.9182 No serial correlation (Fail to reject H₀) 3. Heteroskedasticity (ARCH Test) 184.61 180 0.3912 No ARCH effect (Fail to reject H₀) Source: Compiled by author Diagnostic checks confirm the robustness of the estimated SVAR model. The stability test shows all characteristic roots are within the unit circle, indicating a dynamically stable system. The Portmanteau test finds no evidence of serial correlation in the residuals, and the ARCH test confirms homoskedasticity, validating model consistency. Structural impulse response analysis reveals that an interest rate shock negatively affects GDP growth and inflation in the short term, aligning with theoretical expectations of contractionary monetary policy. These responses diminish over time, highlighting the transitory nature of monetary shocks and the central role of interest rate policy in macroeconomic dynamics. The above graph presents the structural impulse responses of GDP growth, inflation, and interest rate to a one-standard deviation interest rate shock using a Structural VAR (SVAR) framework. The analysis spans ten periods and is underpinned by 95% bootstrap confidence intervals derived from 100 replications. In the immediate aftermath of a shock, there is a significant decline in GDP growth, reflecting the contractionary impact of stricter monetary policy. However, a recovery is observed in the subsequent periods, indicating a temporary positive adjustment before ultimately stabilizing at zero. Inflation also shows an immediate negative reaction, which supports the traditional monetary transmission mechanism. Although there is a slight increase after a few periods, inflation eventually stabilizes, demonstrating the effectiveness of interest rate changes in controlling inflationary pressures In response to its own shock, the interest rate initially surges but then gradually decreases, crossing zero around the fourth period and returning to its baseline level. This pattern illustrates mean reversion and a transient effect. The early responses of GDP and inflation are statistically significant, with confidence intervals not overlapping zero in initial periods. Overall, the SVAR framework provides robust and theoretically grounded insights into macroeconomic dynamics, outperforming standard VAR in capturing the causal structure of policy shocks. SVAR Model Estimation Summary: Short-Run Structural Dynamics of GDP Growth, Inflation, and Interest Rate Table No 9: SVAR Model Summary: Parameter Details Model Type Structural VAR (A-model) Sample Size 63 observations Log Likelihood -346.092 Method Maximum Likelihood / Iterative Number of Iterations 11 Variables Included GDP_Growth_diff, Inflation_diff, InterestRate_diff Identification Short-run Restrictions (A-matrix specified) Source: Compiled by author The summary of the above table presents the estimation results of a Structural Vector Autoregression (SVAR) model analyzing the interdependence among GDP growth, inflation, and interest rates in the U.S. economy. The model includes 63 observations and was estimated using the Maximum Likelihood method with short-run identifying restrictions via an A-matrix specification. The log-likelihood value of -346.092 confirms a satisfactory model fit, and convergence was achieved after 11 iterations, indicating computational stability. The SVAR framework allows for clearer causal inference by imposing theoretically grounded contemporaneous restrictions, enabling a deeper understanding of structural shocks and dynamic macroeconomic relationships. The above graph is Forecast Error Variance Decomposition (FEVD) results from a Structural VAR model examining the dynamic interactions among GDP growth, inflation, and interest rates over a 10-period forecast horizon. The FEVD analysis quantifies the relative importance of own and cross-variable shocks in explaining the forecast error variance of each macroeconomic indicator. For GDP Growth , own shocks are the dominant drivers, contributing approximately 80%–85% of its forecast error variance across all horizons. Inflation shocks explain 10%–12%, while interest rate shocks have a marginal role (5%–8%). This indicates that GDP growth is largely self-driven and relatively insulated from inflation and interest rate shocks in the short-to-medium term. For Inflation , the FEVD reveals that its own shocks account for the majority of its variance (85%–90%), reflecting strong persistence. Interest rate shocks contribute moderately (7%–10%), consistent with the monetary policy transmission mechanism. In contrast, GDP growth shocks exert minimal influence, underscoring the weak direct impact of output on price levels in this model. For Interest Rates , about 50%–55% of forecast error variance is explained by their own shocks. However, inflation shocks play a substantial role (30%–35%), reflecting the Federal Reserve’s responsiveness to inflation fluctuations. GDP growth contributes a smaller portion (10%–15%), suggesting output considerations have a modest but non-trivial effect on interest rate setting. These findings collectively highlight that GDP growth and inflation are significantly shaped by their historical trends, whereas interest rates tend to be more responsive, especially in relation to inflation. The decomposition highlights the asymmetric interdependence among the variables and provides valuable insight into the channels through which monetary policy operates in the U.S. economy. Structural Impact Matrix (B-Matrix) of SVAR Model: Identification Restrictions for GDP Growth, Inflation, and Interest Rate Table No 10: Summary of B Matrix: Source: Compiled by author The Structural Impact Matrix (B-Matrix) employed in the SVAR model adopts a diagonal identity structure to impose short-run identification restrictions. Each macroeconomic variable like GDP growth, inflation, and interest rate is assumed to respond contemporaneously only to its own structural shock, with no immediate cross-variable interactions. This recursive identification strategy, akin to a Cholesky decomposition, simplifies the causal interpretation by assigning instantaneous effects exclusively to the originating variable. Specifically, GDP growth shocks influence only output immediately, inflation shocks affect only price levels, and interest rate shocks impact only monetary policy variables contemporaneously. This structure enhances the model's interpretability and isolates the independent dynamics of each variable in the short run, allowing for a clearer analysis of their structural responses over time. Conclusion This study has revisited macroeconomic causality by proposing a revised Structural Vector Autoregression (SVAR) framework and applying it to the United States economy. The conventional macroeconomic model where interest rates are assumed to lead and shape inflation and GDP growth has been critically re-examined in light of significant empirical and theoretical limitations. Drawing from a comprehensive econometric analysis using ADF tests, VAR diagnostics, impulse response functions (IRFs), and forecast error variance decomposition (FEVD), this research demonstrates that GDP growth and inflation are primarily driven by real-sector and supply-side shocks, whereas interest rates function more as a delayed response mechanism. The empirical findings reinforce a reactive posture of monetary policy, particularly in the context of post-2008 monetary interventions, the COVID-19 pandemic, and the modern inflation regime influenced by global supply chains and structural imbalances. The model's revised causal ordering (Yₜ → πₜ → iₜ) better captures the asymmetrical, non-linear, and lagged policy responses observed in both developed and emerging economies. Moreover, the study bridges multiple theoretical paradigms Keynesian, Post-Keynesian, and elements of the New Keynesian synthesis by validating a structure where output and price levels are not simply policy-reactive but are themselves endogenous and primary drivers of economic adjustment. The global review of literature and empirical models from Japan, the EU, Sub-Saharan Africa, China, and Turkey underscores the universal relevance of this revised framework. Countries with structurally different economic setups nonetheless exhibit similar delays in monetary policy effectiveness, thereby validating the proposed SVAR model as a globally adaptable tool for macroeconomic analysis. By challenging the traditional ordering, this research contributes to a necessary evolution in structural macroeconomic modeling, offering new pathways for policy formulation and academic inquiry in a world where reactive interest rate management is the norm rather than the exception. Policy Recommendations Flexible Inflation Targeting : Policymakers should adopt inflation-targeting frameworks that account for supply-side and global shock drivers rather than rely solely on interest rate adjustments. Strengthen Real-Sector Monitoring : Output and inflation indicators should be closely monitored as leading signals, not merely as lagged responses to interest rate policy. Contextualize Monetary Policy by Region : Recognize that in EMDEs, the interest rate transmission mechanism is even weaker; fiscal and structural reforms must complement monetary actions. Improve International Coordination : In a globalized macroeconomic environment, cross-border spillovers demand that monetary authorities coordinate in their policy outlooks. Transparency in Communication : Central banks should communicate not just rate changes, but also their perceived lag structures and limitations in influencing output and prices. References Investopedia. (n.d.). Macroeconomics: Schools of thought . https://www.investopedia.com/terms/m/macroeconomics.asp Pettinger, T. (2019). Keynesian vs Classical models and policies . Economics Help. https://www.economicshelp.org/keynesian-vs-classical-models-and-policies/ Hoover, K. D. (2008). Phillips Curve . Econlib. https://www.econlib.org/library/Enc/PhillipsCurve.html Sumner, S. (2022). Friedman’s view on inflation . EconLog. https://www.econlib.org/friedmans-view-on-inflation/ Exploring Economics. (2021). Post-Keynesian economics . https://www.exploring-economics.org/en/orientation/post-keynesian-economics/ Investopedia. (n.d.). IS-LM Model . https://www.investopedia.com/terms/i/is-lm-model.asp Wikipedia contributors. (n.d.). AD–AS model . Wikipedia. https://en.wikipedia.org/wiki/AD%E2%80%93AS_model Investopedia. (n.d.). Taylor rule . https://www.investopedia.com/terms/t/taylorrule.asp. Academic Sources Marques, L. B., Casiraghi, M., Gelos, G., Kamber, G., & Meeks, R. (2024). Negative interest rate policies: A survey. Annual Review of Economics, 16 (1), 305–328. https://doi.org/10.1146/annurev-economics-080323-042145 Aoki, K., & Ueda, K. (2025). Survey of the effects of unconventional monetary policy in Japan. The Japanese Economic Review . Nguyen, T. M. L. (2020). Output effects of monetary policy in emerging and developing countries: Evidence from a meta-analysis. Emerging Markets Finance and Trade, 56 (1), 68–85. https://doi.org/10.1080/1540496X.2019.1601081 Balima, H. W., Kilama, E. G., & Tapsoba, R. (2020). Inflation targeting: Genuine effects or publication selection bias? European Economic Review, 128 , 103520. https://doi.org/10.1016/j.euroecorev.2020.103520 Ono, S. (2021). The effects of monetary policy in Russia: A factor-augmented VAR approach. Economic Systems, 45 (3), 100904. https://doi.org/10.1016/j.ecosys.2021.100904 Gregor, J., Melecký, A., & Melecký, M. (2021). Interest rate pass-through: A meta-analysis of the literature. Journal of Economic Surveys, 35 (1), 141–191. https://doi.org/10.1111/joes.12393 Pappas, A., & Boukas, N. (2025). Examining the impact of inflation and inflation volatility on economic growth: Evidence from European Union economies. Economies, 13 (2), 31. https://doi.org/10.3390/economies13020031 Bennett, J., & Owyang, M. T. (2022). On the relative performance of inflation forecasts. Federal Reserve Bank of St. Louis Review, 104 (1), 13–30. https://doi.org/10.20955/r.104.13-30 Haschka, R. E. (2024). Examining the new Keynesian Phillips curve in the U.S.: Why has the relationship between inflation and unemployment weakened? Research in Economics, 78 (4), 101040. https://doi.org/10.1016/j.rie.2024.101040 Binder, C., & Kamdar, R. (2022). Expected and realized inflation in historical perspective. Journal of Economic Perspectives, 36 (3), 131–156. https://doi.org/10.1257/jep.36.3.131 Chen, K., & Kim, J. (2024). Two sided mirror: An analysis of inflation’s dual impact on China’s economic growth. East Asian Economic Review, 28 (2), 175–219. https://doi.org/10.11644/KIEP.EAER.2024.28.2.410 LaBelle, J., & Santacreu, A. M. (2022). Global supply chain disruptions and inflation during the COVID-19 pandemic. Federal Reserve Bank of St. Louis Review, 104 (2), 78–91. https://doi.org/10.20955/r.104.78-91 Ahmed, R., Chen, X. H., Kumpamool, C., & Nguyen, D. T. K. (2023). Inflation, oil prices, and economic activity in recent crisis: Evidence from the UK. Energy Economics, 126 , 106918. https://doi.org/10.1016/j.eneco.2023.106918 Graphs Graphs 1-3 are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. 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P M Savadatti","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYPACZiBmbHzAwJAA5h4gSgsPG2OzAalaGNgkYFrwAnOx4w8/89RYy9vLN7dV/GxLY+BvP8B4uACPFsvZOcbSPMfSDXvYGNtu9rblMEicSWA4PAOPFoPbOQySM9gOM4K03GZsq2BguMHAcJgHr5b0xz9n/DtsD9JSDNIiT1hLgpnEx7bDiSAtzIxAhxkQ0gL0i5nFx7705J5jic2SPefSeAzPJDbg1WIunf74RsI3a9v25uMPP/woS5aTO3748Ge8DkMXACpmbMCjAYuWUTAKRsEoGAUYAAB16ksIPQEl4QAAAABJRU5ErkJggg==","orcid":"","institution":"Central University Kalaburgi, Karnataka","correspondingAuthor":true,"prefix":"","firstName":"Prf.","middleName":"P M","lastName":"Savadatti","suffix":""}],"badges":[],"createdAt":"2025-05-22 03:37:18","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6720708/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6720708/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83429483,"identity":"6f82d20c-2cc2-439c-b8be-0745fd46770b","added_by":"auto","created_at":"2025-05-26 06:25:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1889741,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6720708/v1/c0d795f5-6012-4047-8f3d-7cdf74271300.pdf"},{"id":83428957,"identity":"17e1acaa-2fc7-4c5b-8100-3d3e19ba65f1","added_by":"auto","created_at":"2025-05-26 06:17:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":522253,"visible":true,"origin":"","legend":"","description":"","filename":"Graphs.docx","url":"https://assets-eu.researchsquare.com/files/rs-6720708/v1/09a64ce5a4e822882342a422.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eRethinking Global Macroeconomic Causality: A Structural VAR Model Based on U.S. Evidence\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe complex relationship among GDP growth, inflation, and interest rates continues to be a fundamental focus in the analysis of macroeconomic policy. Traditional macroeconomic theories, such as the Phillips Curve (Phillips, 1958) and the Monetary Policy Transmission Mechanism (Bernanke \u0026amp; Gertler, 1995), postulate that interest rate policies by central banks influence inflation and output growth, typically assuming an immediate reaction of financial variables to policy adjustments. However, recent structural shifts in the global economy including post-2008 unconventional monetary policies, the COVID-19 pandemic, and supply chain disruptions suggest that these relationships have changed (Borio \u0026amp; Disyatat, 2015; Forbes et al., 2021; IMF, 2020).\u003c/p\u003e\n\u003cp\u003eIn this research, Researcher reexamine the relationship among GDP growth, inflation, and interest rates within the framework of the United States economy, utilizing a Structural Vector Autoregression (SVAR) Model. And a researcher proposed a revised causal ordering based on observed economic patterns in the last two decades. Specifically, study hypothesize that output (GDP growth) and inflation are immediately affected by domestic and global shocks, whereas interest rate adjustments by the Federal Reserve follow these developments with a lag, reflecting the central bank\u0026rsquo;s reactive stance (Clarida, Gali, \u0026amp; Gertler, 2000).\u003c/p\u003e\n\u003cp\u003eThis approach not only aligns with recent literature highlighting the delayed transmission of monetary policy (Borio \u0026amp; Disyatat, 2015) but also incorporates the increasing influence of supply-side factors and external shocks on inflation (Forbes et al., 2021). Through this analysis, researcher aimed to provide a more accurate interpretation of macroeconomic policy effectiveness in a dynamically changing economic environment.\u003c/p\u003e"},{"header":"Motivation for the Study","content":"\u003cp\u003eThe relationship among GDP growth, inflation, and interest rates is fundamental to the analysis of macroeconomics and the development of policy. Traditionally, economic models assume that interest rates controlled by central banks serve as the primary tool for influencing output and inflation. However, recent global disruptions, including the 2008 financial crisis, the COVID-19 pandemic, and ongoing supply chain bottlenecks, have altered the landscape of macroeconomic interactions.\u003c/p\u003e\n\u003cp\u003eThese structural shifts raise critical questions about the validity of the conventional causal ordering used in macroeconomic modeling. Empirical observations from the past two decades show that monetary policy often reacts to inflation and output trends rather than proactively shaping them. Moreover, supply-side factors and external shocks now play a dominant role in shaping inflation dynamics, reducing the immediate effectiveness of interest rate adjustments.\u003c/p\u003e\n\u003cp\u003eThis evolving economic reality demands a re-examination of traditional models. By employing a Structural VAR Model, this research aims to present new empirical evidence indicating that GDP growth and inflation respond to structural shocks simultaneously, whereas interest rates adjust with a delay, reflecting a reactive approach to policy. The motivation for this study lies in bridging the gap between traditional macroeconomic theory and current empirical observations. It aims to enhance our understanding of macroeconomic policy effectiveness in today\u0026rsquo;s uncertain and rapidly changing global economic environment, thereby contributing both to academic literature and policy-making discourse.\u003c/p\u003e"},{"header":"Review of Literature ","content":"\u003cp\u003eComparative Review of Macroeconomic Theories and Their Relevance to the Study\u003c/p\u003e\n\u003cp\u003eUnderstanding the dynamic interactions between GDP growth, inflation, and interest rates has been a central concern in macroeconomic theory. The present study revisits this relationship through a revised Structural VAR (SVAR) framework, drawing upon and challenging the assumptions embedded in classical, Keynesian, monetarist, New Keynesian, and Post-Keyesian paradigms. Below is a synthesis of how these schools of thought interpret the mechanisms of macroeconomic adjustment and how these views frame the motivation behind rethinking causal ordering.\u003c/p\u003e\n\u003cp\u003eClassical versus Keynesian Approaches\u003c/p\u003e\n\u003cp\u003eClassical economics postulates that markets possess self-correcting mechanisms, suggesting that any deviations from full employment are only temporary. In this context, inflation is viewed solely as a monetary issue, with interest rates adjusting to balance savings and investment, thereby negating any long-term trade-off between inflation and output. As a result, interest rate shocks are treated as exogenous and immediate, aligning seamlessly with a Cholesky-ordered SVAR structure where monetary policy is the leading factor.\u003c/p\u003e\n\u003cp\u003eIn contrast, Keynesian economics argues that economies can experience extended periods of insufficient demand. It recognizes that prices and wages tend to be rigid, and that interest rates, influenced by central banks, have a significant impact on investment and output in the short term. The Keynesian model endorses a more endogenous perspective on interest rates and emphasizes the importance of aggregate demand in driving GDP growth. This aligns with the hypothesis of this study, which posits that GDP and inflation react to real sector shocks, while interest rates tend to respond with a delay.\u003c/p\u003e\n\u003cp\u003eKeynesian versus Monetarist Theories\u003c/p\u003e\n\u003cp\u003eBoth economic schools acknowledge the short-term effectiveness of economic policy but diverge in their preferred tools. Monetarists, particularly Milton Friedman, advocate for the regulation of money supply growth, asserting that inflation is fundamentally a monetary phenomenon. They also caution against the use of activist fiscal policies. However, the empirical evidence from our study reflects a Keynesian viewpoint, indicating that in recent years, central banks have frequently opted for delayed interest rate adjustments rather than proactively leading economic cycles.\u003c/p\u003e\n\u003cp\u003eThis observed delay, particularly evident in the monetary policy following the 2008 financial crisis and the stimulus measures during the COVID-19 pandemic, prompts us to reconsider the traditional SVAR ordering. Monetarist models, which assume that inflation responds swiftly to changes in the money supply, find it challenging to account for the sustained low inflation observed during prolonged periods of monetary expansion a conundrum that our SVAR analysis seeks to resolve. \u003c/p\u003e\n\u003cp\u003eMonetarist vs. New Keynesian Synthesis\u003c/p\u003e\n\u003cp\u003eNew Keynesians incorporate rational expectations and micro foundations, enhancing the analytical rigor of Keynesian insights. They transition their emphasis from targeting the money supply to implementing interest rate guidelines, like the Taylor Rule, which connects interest rate decisions to inflation levels and output discrepancies. \u0026nbsp;This transition reflects contemporary monetary policy frameworks where central banks aim to stabilize inflation expectations rather than control money supply.\u003c/p\u003e\n\u003cp\u003eOur study extends this logic but questions the assumption of policy leadership implicit in these models. Using SVAR, Researcher test whether interest rates really lead and influence GDP growth and inflation, or if they lag as a reactive tool a view supported by the empirical evidence of delayed monetary responses and nonlinear inflation dynamics.\u003c/p\u003e\n\u003cp\u003eNew Keynesian vs. Post-Keynesian Perspectives\u003c/p\u003e\n\u003cp\u003ePost-Keynesians critique the mainstream focus on equilibrium modeling and rational expectations. They highlight the importance of structural uncertainty, financial instability, and the demand-driven nature of long-term output. Their analysis centers on factors such as cost-push inflation, conflicts in income distribution, and the endogeneity of money and interest rates.\u003c/p\u003e\n\u003cp\u003eThis alternative perspective closely aligns with the theoretical revisions presented in our study. We contend that supply shocks, fluctuations in demand, and institutional delays require a reconfiguration of causal relationships within SVAR models. Rather than viewing interest rates as exogenous shocks, our model treats them as endogenously influenced by prior changes in GDP and inflation, which supports the Post-Keynesian view of a demand-oriented and institutionally grounded macroeconomic framework.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIncorporating Empirical Models: IS-LM, AD-AS, Phillips Curve, and Taylor Rule\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile macroeconomic theory lays the groundwork with its fundamental assumptions, empirical models are essential for simulating the complexities of real-world dynamics:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIS-LM Model:\u003c/strong\u003e Demonstrates the relationship between interest rates and economic output, under the assumption of constant prices in the short term. Our research builds on this by examining if interest rates actually respond after output changes, not before.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAD-AS Model\u003c/strong\u003e: Reflects both demand and supply factors in determining price and output levels. The SVAR model adopted here reflects such dual shocks, particularly in accounting for inflation\u0026apos;s response to supply bottlenecks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhillips Curve\u003c/strong\u003e: Traditionally portrayed a trade-off between inflation and unemployment. Our findings support recent critiques that this relationship has weakened due to global supply chains and technology, necessitating revised modeling strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTaylor Rule\u003c/strong\u003e: Offers a policy benchmark linking interest rates to inflation and output gaps. Our SVAR estimation, however, calls into question the underlying assumption that central banks operate in a contemporaneous or proactive manner.\u003c/p\u003e\n\u003cp\u003eThis research situates itself at the intersection of macroeconomic theory and econometric innovation. By revisiting causal ordering in a structural framework, the study not only builds on but critically evaluates the assumptions of traditional models. While Classical and Monetarist views offer long-run insights, they often miss short-run policy lags and structural shocks. Keynesian and New Keynesian frameworks accommodate these realities but still presume a leading role for interest rates in steering the economy.\u003c/p\u003e\n\u003cp\u003ePost-Keynesian critique and real-world developments such as delayed Fed responses, persistent inflation, and global shocks demand a new perspective. This study contributes by reordering causality, placing GDP growth and inflation ahead of interest rate adjustments, which better reflects contemporary macroeconomic behavior and enhances the explanatory power of SVAR modeling in policy research.\u003c/p\u003e\n\u003cp\u003eReviewed Research Papers\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBrand\u0026atilde;o‑Marques et al. (2024) \u0026ndash; Negative Interest Rate Policies: A Survey\u003c/p\u003e\n\u003cp\u003eThis comprehensive review analyzes the theoretical rationale, empirical impacts, and global experiences with negative interest rate policies (NIRPs). The authors explore how NIRPs, adopted by central banks in the Eurozone, Japan, and others, affect output, inflation, and financial stability. The study identifies transmission mechanisms and limitations, particularly the diminished effectiveness in low-rate environments. It reinforces the idea that the effects of monetary policy can differ depending on the context, thereby questioning the straightforward relationship between interest rates and inflation. this is particularly relevant to our SVAR-based rethinking of policy lags and effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAoki \u0026amp; Ueda (2025)\u003c/strong\u003e \u0026ndash; This study explores Japan\u0026apos;s implementation of unconventional monetary policy measures, including quantitative easing (QE), yield curve control (YCC), and negative interest rates, aimed at addressing ongoing low inflation and stagnant GDP growth. The authors assess the effectiveness of these strategies through empirical analyses utilizing VAR, SVAR, and DSGE models. They conclude that while monetary easing has led to modest improvements in GDP growth, its influence on inflation expectations has been limited. Their results support the notion that in economies facing structural constraints, inflation does not always respond to interest rate fluctuations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNguyen (2020)\u0026nbsp;\u003c/strong\u003eNguyen performs a meta-analysis encompassing over 45 studies to investigate the effects of monetary tightening on output in emerging and developing economies (EMDEs). The findings consistently indicate that contractionary monetary policy results in significant reductions in GDP growth, although the extent of these effects varies depending on institutional quality and economic openness. The research highlights the variability in policy impacts across different economies, affirming the necessity of employing differentiated structural models such as SVARs. This study supports the argument that both global and domestic shocks should be meticulously disaggregated to accurately evaluate causal relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBalima et al. (2020)\u003c/strong\u003e This Study employs meta-regression analysis on over 8,000 estimates to evaluate the credibility and effectiveness of inflation targeting (IT). The research reveals that, even after adjusting for selection biases, IT regimes are linked to lower inflation rates and marginally improved growth performance. This finding is noteworthy as it implies that stability in inflation may precede GDP responses, challenging the conventional output-to-inflation relationship. Additionally, the paper indirectly supports models like our that reconsider lag structures and the responsiveness of policy under varying macroeconomic conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOno (2021)\u003c/strong\u003e \u0026ndash; Monetary Policy in Russia: A Factor-Augmented VAR Approach\u003c/p\u003e\n\u003cp\u003eOno employs a factor-augmented VAR (FAVAR) model to investigate the macroeconomic reactions of Russia to monetary shocks. The research reveals that unexpected increases in interest rates result in heightened inflation, primarily influenced by exchange rate pass-through and imported inflation. This surprising outcome challenges conventional macroeconomic theories and underscores the necessity to reconsider the traditional causal sequences in VAR/SVAR models. It lends empirical support to the notion that GDP and inflation respond initially, followed by adjustments in interest rates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGregor, Meleck\u0026yacute; \u0026amp; Meleck\u0026yacute; (2021)\u0026nbsp;\u003c/strong\u003e\u0026ndash; This review synthesizes results from 54 empirical studies that explore the transmission of central bank policy rates to lending and deposit rates across various countries. The meta-analysis indicates that the pass-through effect is frequently incomplete and varies considerably depending on market structure, inflation targeting strategies, and the credibility of monetary policy. The results imply that the anticipated transmission of interest rate policy to macroeconomic indicators such as GDP and inflation is neither consistent nor immediate, reinforcing the concept of a delayed policy response as suggested in our SVAR model framework.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePappas \u0026amp; Boukas (2025)\u003c/strong\u003e \u0026ndash;This study conducts an empirical analysis of the combined effects of inflation and inflation volatility on GDP growth within EU nations. The findings indicate that both elevated inflation and fluctuating inflation levels significantly hinder long-term growth. The methodology includes panel data econometrics and robust cross-country regressions. The paper\u0026rsquo;s conclusion aligns with our research that inflation\u0026rsquo;s impact on output is often non-linear and context-dependent, thereby reinforcing the relevance of flexible causal orderings in structural modeling frameworks.\u003c/p\u003e\n\u003cp\u003eBennett \u0026amp; Owyang (2022) \u0026ndash;\u0026nbsp;This review investigates why most institutional and private inflation forecasts particularly those in the U.S. underpredicted the inflation surges post-COVID. It reveals systematic forecast errors driven by model misspecification, especially underestimating the effects of supply shocks and stimulus-driven demand surges. The authors argue for updated modeling strategies that account for asymmetries and delayed effects in the inflation-output-interest rate nexus.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHaschka (2024) \u0026ndash;\u0026nbsp;Haschka analyzes the declining explanatory power of the Phillips Curve using U.S. macro data. The study shows that since the 1980s, and especially post-2008, the inflation-unemployment trade-off has weakened. Technological changes, labor market slack, and better-anchored inflation expectations are identified as causes. The review employs various structural models, including New Keynesian DSGEs and SVARs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBinder \u0026amp; Kamdar (2022) \u0026ndash;\u0026nbsp;This study examines historical episodes where inflation expectations diverged significantly from realized inflation e.g., the 1970s Great Inflation vs. the post-COVID inflation surge. The authors conclude that well-anchored expectations reduce the cost of monetary tightening on growth. Their analysis draws from multiple time-series and SVAR models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChen \u0026amp; Kim (2024)\u0026nbsp;This study explores the nonlinear relationship between inflation and GDP growth in China using a threshold SVAR model. The authors find that when inflation is below 2%, it has a mildly positive effect on economic growth due to increased price signals and moderate demand expansion. However, inflation above this threshold leads to a sharp decline in GDP growth due to rising costs and uncertainty. This nuanced view underscores the importance of considering regime-switching behavior in inflation\u0026rsquo;s macroeconomic effects. The study strengthens our argument that inflation should not be treated as a linear function of interest rates or GDP, but rather as a dynamic response with feedback effects.\u003c/p\u003e\n\u003cp\u003eLaBelle \u0026amp; Santacreu (2022)\u003c/p\u003e\n\u003cp\u003eThis study reviews the inflationary impact of pandemic-induced global supply chain disruptions. It estimates that more than half of the U.S. core inflation increase in 2021\u0026ndash;22 can be attributed to supply-side bottlenecks rather than demand-side overheating. The study uses decomposition techniques and structural models to isolate the drivers of inflation. The findings challenge traditional views of monetary transmission, suggesting that interest rate hikes are ineffective against supply-driven inflation. This supports our revised SVAR causal ordering, where inflation emerges from real-sector shocks before interest rate responses.\u003c/p\u003e\n\u003cp\u003eAhmed et al. (2023)\u003c/p\u003e\n\u003cp\u003eThis UK-focused study employs SVAR modeling to examine how oil price shocks influence inflation and GDP growth. The findings indicate that oil prices pass through rapidly to inflation, while GDP responds negatively with a delay. Interestingly, monetary tightening is shown to reduce output in the short run without immediately dampening inflation. This time lag between interest rate decisions and macroeconomic impact reinforces our hypothesis that central banks often act in response to existing inflation and growth trends rather than preemptively.\u003c/p\u003e\n\u003cp\u003eOlaoye et al. (2024)\u003c/p\u003e\n\u003cp\u003eThe authors conduct a panel analysis of 44 Sub-Saharan African countries to investigate the primary causes of inflation. Their findings reveal that inflation is largely driven by fiscal imbalances specifically rising public debt and budget deficits rather than money supply or direct interest rate changes. The study challenges monetary-dominant narratives and shows that inflation often emerges from the real and fiscal sectors. This reinforces the logic in our SVAR model that inflation and GDP respond to structural shocks first, with interest rates adjusting later based on fiscal conditions.\u003c/p\u003e\n\u003cp\u003eCieslak \u0026amp; Pflueger (2023)\u003c/p\u003e\n\u003cp\u003eThis review connects macroeconomic inflation dynamics with financial market performance, focusing on asset pricing under different inflation regimes. It shows that unexpected inflation typically depresses both equity and bond returns due to uncertainty and eroded real yields. However, in certain scenarios, credible monetary tightening can stabilize expectations and even support asset prices. The authors emphasize the importance of inflation expectations and central bank credibility. Their findings provide indirect but powerful support for treating inflation as a pivotal channel influencing monetary response in our SVAR Model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDr\u0026auml;ger \u0026amp; Lamla (2024)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis Study reviews contemporary research regarding how consumers develop expectations concerning inflation, interest rates, and future GDP. The authors contend that consumer expectations frequently diverge from expert predictions and are shaped by recent personal experiences, media coverage, and noticeable price fluctuations (such as those in fuel or food). These behavioral inconsistencies pose challenges for effective monetary policy, as the results of such policies depend not only on changes in interest rates but also on public perception of those changes. This finding reinforces our revised model structure by indicating that inflation expectations, and consequently inflation itself, are only loosely tied to central bank actions in the short term.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAkalpler (2025)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAkalpler\u0026rsquo;s research investigates Turkey\u0026rsquo;s unconventional monetary policy, which emphasized GDP growth through low interest rates despite escalating inflation. The results indicate short-term GDP improvements but a significant rise in inflation and currency devaluation. This policy approach resulted in long-term macroeconomic instability, demonstrating the limitations of demand-driven growth amid high inflationary pressures. The author points out that changes in interest rates were reactive to economic developments rather than proactive. This observation aligns perfectly with our updated SVAR framework, where shocks to output and inflation occur prior to adjustments in policy rates.\u003c/p\u003e\n\u003cp\u003eBayir \u0026amp; Orak (2024) This empirical research analyzes inflation dynamics in five major emerging economies: Brazil, Russia, India, China, and Turkey. Utilizing panel VAR methodologies, the authors discover that fluctuations in exchange rates and output growth exert a more immediate and significant impact on inflation compared to movements in interest rates. The findings affirm that in emerging markets, monetary policy instruments are frequently limited by global shocks and capital flows. These results bolster our model\u0026apos;s hypothesis that inflation is primarily driven by real economic shocks, with interest rate adjustments occurring later in the response cycle.\u003c/p\u003e\n\u003cp\u003eBauer \u0026amp; Rudebusch (2020) This review examines the persistent decline in natural interest rates, referred to as \u0026ldquo;r-star,\u0026rdquo; in developed nations. The authors identify several contributing factors to this trend, including demographic shifts, reduced productivity, and a heightened global appetite for safe assets. As real interest rates continue to decrease, central banks are confronted with the zero-lower-bound issue, which restricts their capacity to combat inflation or foster growth through traditional interest rate adjustments. The findings strongly endorse the notion that monetary policy has shifted towards a more reactive stance rather than a proactive one, thereby validating the use of a SVAR framework in which interest rates respond with a delay.\u003c/p\u003e\n\u003cp\u003eHa, Kose \u0026amp; Ohnsorge (2022)\u003c/p\u003e\n\u003cp\u003eThis comprehensive review analyzes the evolution of inflation dynamics in emerging markets and developing economies (EMDEs) from the high-inflation era of the 1970s to the relatively stable period of the 2010s, culminating in the inflation surge following the COVID-19 pandemic. The authors point out that structural reforms, credible inflation targeting, and improved fiscal discipline have enabled many EMDEs to manage inflation effectively until global supply shocks reintroduced instability. Notably, the paper underscores that recent inflationary trends are increasingly driven by supply factors and have a global dimension. This observation reinforces the foundational assumption of our SVAR model inflation shocks are now more independent and less directly linked to domestic interest rate policies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Gap\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough there is a significant amount of empirical and theoretical research regarding the dynamic relationships among GDP growth, inflation, and interest rates, a notable gap remains in comprehending how the causal relationships among these variables have changed due to structural transformations in the global economy.\u003c/p\u003e\n\u003cp\u003eTraditional macroeconomic theories ranging from Classical and Monetarist views to New Keynesian frameworks largely assume a policy-led approach where central bank interest rates precede and influence inflation and output, forming the conventional ordering used in Structural VAR (SVAR) modeling (Brand\u0026atilde;o‑Marques et al., 2024; Balima et al., 2020; Nguyen, 2020).\u003c/p\u003e\n\u003cp\u003eHowever, emerging evidence challenges these assumptions. Recent studies show that interest rate responses are often delayed or ineffective in influencing inflation, especially when inflation is driven by exogenous supply shocks or global fiscal spillovers (LaBelle \u0026amp; Santacreu, 2022; Bennett \u0026amp; Owyang, 2022; Ha, Kose \u0026amp; Ohnsorge, 2022). For instance, Aoki \u0026amp; Ueda (2025) demonstrate that Japan\u0026apos;s unconventional monetary policy tools yielded limited effects on inflation expectations, while Ahmed et al. (2023) and Bayir \u0026amp; Orak (2024) show that inflation and GDP growth often react first to real-sector disturbances such as oil price shocks or exchange rate fluctuations, followed later by monetary policy adjustments.\u003c/p\u003e\n\u003cp\u003eFurthermore, empirical studies across diverse economic settings ranging from developed economies like the U.S. and EU to emerging markets such as Turkey, India, and Sub-Saharan Africa confirm the asymmetry and delay in monetary transmission mechanisms (Ono, 2021; Olaoye et al., 2024; Akalpler, 2025). These findings reveal that real economic shocks now increasingly drive inflation and output, with interest rates acting more as reactive stabilization tools than proactive levers (Cieslak \u0026amp; Pflueger, 2023; Dr\u0026auml;ger \u0026amp; Lamla, 2024).\u003c/p\u003e\n\u003cp\u003eDespite these insights, most SVAR-based studies continue to adopt a traditional causal structure where interest rate shocks lead the system a structure that may no longer reflect contemporary macroeconomic dynamics. While some works (e.g., Chen \u0026amp; Kim, 2024) begin to address nonlinear and threshold effects, few studies have explicitly restructured SVAR models to reverse the standard ordering of interest rate, inflation, and GDP growth based on post-2008 realities and post-pandemic monetary policy behavior.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis creates a two-fold research gap\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eTheoretical Gap\u003c/strong\u003e: A disconnect between evolving macroeconomic realities (e.g., inflation persistence, supply shocks, delayed policy reactions) and the conventional theoretical foundations of SVAR ordering grounded in outdated assumptions.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEmpirical Gap\u003c/strong\u003e: A lack of SVAR-based studies especially in the context of the United States that systematically test alternative causal orderings, such as placing GDP growth and inflation as contemporaneously endogenous variables and treating interest rate adjustments as lagged, policy-reactive responses.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTherefore, this study aims to bridge this gap by developing and empirically validating a \u003cstrong\u003erevised SVAR framework\u003c/strong\u003e that rethinks the causal sequencing of GDP growth, inflation, and interest rates. In doing so, it contributes not only to the econometric modeling literature but also to a more accurate understanding of macroeconomic policy effectiveness in today\u0026apos;s volatile global environment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eTo empirically examine the causal relationship between GDP growth, inflation, and interest rates in the United States using a SVAR.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTo challenge and re-evaluate causal ordering used in SVAR models, by testing a revised sequence wherein GDP growth and inflation precede interest rate adjustments.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eModel and Theory Justification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp;\u003cstrong\u003eJustification for Using Structural VAR (SVAR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Structural Vector Autoregression (SVAR) framework has been selected for this research because of its strong ability to identify dynamic interrelationships among macroeconomic variables, while also facilitating the theoretical identification of structural shocks. In contrast to conventional VAR models, which lack a theoretical foundation and only reveal reduced-form relationships, the SVAR model applies economically relevant constraints derived from theory to distinguish the effects of both policy and non-policy shocks.\u003c/p\u003e\n\u003cp\u003eGiven the objective of re-examining the causal order among GDP growth (Yₜ), inflation (\u0026pi;ₜ), and interest rates (iₜ), SVAR provides an ideal platform because it enables:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eIdentification of contemporaneous structural innovations.\u003c/li\u003e\n \u003cli\u003eMapping of impulse responses to specific economic shocks.\u003c/li\u003e\n \u003cli\u003eTesting of alternative causal orderings via imposed restrictions in the \u003cstrong\u003eA\u003c/strong\u003e and \u003cstrong\u003eB\u003c/strong\u003e matrices.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis study adopts a \u003cstrong\u003eshort-run (contemporaneous) restriction-based SVAR\u003c/strong\u003e using an \u003cstrong\u003eA-model\u003c/strong\u003e formulation, where the structure is justified by evolving macroeconomic realities, including delayed monetary policy response and real-sector-driven inflation.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eJustification for the Revised Causal Ordering: Yₜ \u0026rarr; \u0026pi;ₜ \u0026rarr; iₜ\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConventional SVAR models in macroeconomics typically assume that \u003cstrong\u003einterest rate shocks precede and influence inflation and output\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e which mirrors the \u003cstrong\u003emonetarist and New Keynesian\u003c/strong\u003e belief that central banks proactively steer the economy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis study contests that \u003cu\u003eAssumptions\u003c/u\u003e by introducing an updated causal framework in which\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eGDP growth (Yₜ)\u003c/strong\u003e reacts contemporaneously to domestic and global demand/supply shocks.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eInflation (\u0026pi;ₜ)\u003c/strong\u003e adjusts immediately to both output conditions and supply chain factors.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eInterest rates (iₜ)\u003c/strong\u003e respond with a \u003cstrong\u003elag\u003c/strong\u003e, reflecting the reactive nature of monetary policy in recent decades.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eThis revised ordering is theoretically supported by: Post-Keynesian economics\u003c/strong\u003e, which views interest rates as an endogenous policy instrument.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eEmpirical findings showing that \u003cstrong\u003emonetary policy responses often lag behind inflationary and growth trends\u003c/strong\u003e (e.g., Aoki \u0026amp; Ueda, 2025; LaBelle \u0026amp; Santacreu, 2022).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe fading effectiveness of interest rates during \u003cstrong\u003ezero lower bound environments\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e further justifying their placement as lagged responses in the system.\u003c/p\u003e"},{"header":"Research Methodology","content":"\u003cp\u003eThis study employs a quantitative, time-series econometric approach using a Structural Vector Autoregression (SVAR) Model. The central objective is to empirically test a revised causal ordering between GDP growth (Yₜ), inflation (\u0026pi;ₜ), and interest rate (iₜ) in the context of the United States economy. The methodology integrates both theoretical model-building and empirical validation to re-express macroeconomic relationships in light of contemporary policy lags, structural shocks, and post-crisis dynamics. Unlike conventional SVARs that place interest rates as the leading variable, this research introduces a different identification framework in which output and inflation react simultaneously to structural shocks, while interest rates adjust with a delay.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Table No 1: Data Source and Description\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"582\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDetails\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime Period\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1961 to 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnnual\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables Used\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDP Growth (Yₜ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReal GDP growth rate (Annual %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInflation (\u0026pi;ₜ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConsumer Price Index (CPI) \u0026ndash; Annual % change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInterest Rate (iₜ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFederal Funds Rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Compiled by author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Preprocessing and Stationarity Testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore SVAR estimation, all three series are tested for stationarity using the Augmented Dickey-Fuller (ADF) test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable No 2: \u0026nbsp;ADF test results\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"582\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eADF Statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStationarity Decision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDP Growth (Yₜ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.7158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStationary at Level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInflation (\u0026pi;ₜ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.6294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-stationary \u0026rarr; Differenced\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInterest Rate (iₜ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.5921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-stationary \u0026rarr; Differenced\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Compiled by author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Model Specification: SVAR Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe reduced-form VAR model is expressed as:\u003c/p\u003e\n\u003cp\u003e[Yₜ, \u0026pi;ₜ, iₜ] = C + A₁ \u0026nbsp;[Yₜ₋₁, \u0026pi;ₜ₋₁, iₜ₋₁] + \u0026epsilon;ₜ\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cp\u003e- C: Vector of intercepts\u003c/p\u003e\n\u003cp\u003e- A₁: Matrix of lagged coefficients\u003c/p\u003e\n\u003cp\u003e- \u0026epsilon;ₜ ~ N(0, \u0026Sigma;): Vector of reduced-form errors\u003c/p\u003e\n\u003cp\u003e- Structural shocks uₜ are retrieved using the transformation \u0026epsilon;ₜ = B uₜ\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification Strategy (Short-Run Restrictions)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study adopts short-run (recursive) restrictions with the following structural ordering:\u003c/p\u003e\n\u003cp\u003eYₜ \u0026rarr; \u0026pi;ₜ \u0026rarr; iₜ\u003c/p\u003e\n\u003cp\u003eThis implies:\u003c/p\u003e\n\u003cp\u003e- GDP growth (Yₜ) responds only to its own structural shocks contemporaneously.\u003c/p\u003e\n\u003cp\u003e- Inflation (\u0026pi;ₜ) responds to both GDP and its own shocks.\u003c/p\u003e\n\u003cp\u003e- Interest Rate (iₜ) responds with a lag and is influenced by past output and inflation conditions.\u003c/p\u003e\n\u003cp\u003eThe B matrix for structural identification is defined as an identity matrix:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;[1 0 0]\u003c/p\u003e\n\u003cp\u003eB = \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;[0 1 0]\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;[0 0 1]\u003c/p\u003e\n\u003cp\u003eThis simplified structure assumes no contemporaneous cross-effects except from structural innovations within each variable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Model Estimation and Diagnostics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SVAR model is estimated through maximum likelihood estimation, utilizing a lag length of 1 as determined by information criteria such as AIC and BIC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic Tests Conducted:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e- Stability Test: All characteristic roots lie inside the unit circle \u0026rarr; Model is stable.\u003c/p\u003e\n\u003cp\u003e- Serial Correlation Test (Portmanteau): No autocorrelation detected (p = 0.9182).\u003c/p\u003e\n\u003cp\u003e- Heteroskedasticity Test (ARCH): No heteroskedasticity in residuals (p = 0.3912).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytical Tools and Procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe following tools are used to evaluate dynamic relationships:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e- Impulse Response Functions (IRFs): This method evaluates how each variable reacts over time to a structural shock.\u003c/p\u003e\n\u003cp\u003e- Forecast Error Variance Decomposition (FEVD): This technique examines the extent to which forecast errors in each variable can be traced back to specific structural shocks.\u003c/p\u003e\n\u003cp\u003e- Residual Correlation Matrix: To ensure residual independence.\u003c/p\u003e\n\u003cp\u003e- Structural Impact Matrix (B-Matrix): To implement identification restrictions. All analyses are conducted utilizing R Studio,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJustification of Methodological Choice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSVAR is superior to VAR in identifying causal mechanisms in a theoretically grounded manner. The revised structural ordering tested in this model reflects contemporary macroeconomic realities lagged policy reactions, inflation inertia, and real-sector dominance. Using Time-Series techniques with rigorous pre-model testing ensures validity and robustness of inferences.\u003c/p\u003e"},{"header":"Results and Discussions","content":"\u003cp\u003eTable No 3: Descriptive summary table:\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicator Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinimum (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGDP Growth (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInflation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInterest Rate (Federal Reserve System USA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Compiled by author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe above table presents a descriptive analysis of key macroeconomic indicators of the United States, specifically focusing on GDP growth, inflation rates, and the Federal Reserve\u0026rsquo;s interest rates over the observed period. The findings reveal that GDP growth rates fluctuated between a minimum of -2.60%, indicating recessionary periods, and a maximum of 7.12%. The average growth rate was noted at 3.00%, with a standard deviation of 2.06%, suggesting a moderate degree of variability in economic growth.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Inflation rates exhibited greater volatility, fluctuating between -0.03% and 11.80%, with a mean of 3.84% and a standard deviation of 2.76%. The Federal Reserve\u0026rsquo;s interest rates demonstrated significant variability, ranging from a low of 0.08% to a peak of 16.40%, averaging 4.80% with a standard deviation of 3.57%. These descriptive statistics highlight the dynamic nature of the U.S. economy, marked by fluctuations in growth, price stability, and monetary policy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStationary Check\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable No 4: Consolidated ADF Test Results\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDickey-Fuller Test Statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLag Order\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStationarity Decision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDP (gdp_ts)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.7158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStationary\u003c/strong\u003e (Reject H₀ at 1% level)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInflation (inflation_ts)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.6294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Stationary\u003c/strong\u003e (Fail to reject H₀)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInterest Rate (interest_ts)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.5921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Stationary\u003c/strong\u003e (Fail to reject H₀)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Compiled by author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ethe above table Augmented Dickey-Fuller (ADF) test on three core U.S. macroeconomic time series GDP growth, inflation, and interest rates to assess their stationarity. The results indicate that GDP growth is stationary at level, while both inflation and interest rate series exhibit non-stationarity and require first differencing. These findings are critical for ensuring the reliability of time series models such as VAR and SVAR, as incorporating non-stationary variables without transformation may lead to spurious results. The analysis confirms the necessity of pre-testing for unit roots in empirical macroeconomic research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsolidated Summary and Interpretation of ADF test and VAR Estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable No 5: Stationarity Diagnosis Using ADF Test\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eADF Test Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStationarity at Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAction Taken\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGDP Growth (gdp_ts)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.7158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStationary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUsed as is (difference stationary)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInflation (inflation_ts)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.6294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-Stationary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDifferenced once\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInterest Rate (interest_ts)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.5921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-Stationary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDifferenced once\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Compiled by author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe above table presents unit root diagnostics using the Augmented Dickey-Fuller (ADF) test for three key macroeconomic variables: GDP growth, inflation, and interest rates. The test reveals that GDP growth is stationary at level, indicating that it can be used in its original form for time series modeling. In contrast, inflation and interest rate series are non-stationary at level with p-values above conventional significance thresholds. To ensure valid inference and model stability, these two series were differenced once to achieve stationarity. These transformations form the foundational step for further econometric modeling using VAR and SVAR frameworks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable No 6: Equation-Based Estimates of the VAR Model: Dynamic Interactions Between GDP Growth, Inflation, and Interest Rates (Differenced Series)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDependent Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndependent Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSignificance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGDP_Growth_diff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGDP_Growth_diff.l1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.54363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.99e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGDP_Growth_diff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInflation_diff.l1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.76082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGDP_Growth_diff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInterestRate_diff.l1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.28266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.137799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInflation_diff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGDP_Growth_diff.l1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.05644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.50172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInflation_diff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInflation_diff.l1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.34762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInflation_diff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInterestRate_diff.l1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.32591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInterestRate_diff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGDP_Growth_diff.l1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.16412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInterestRate_diff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInflation_diff.l1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.64476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.52e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInterestRate_diff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInterestRate_diff.l1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.23314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Compiled by author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis table displays the findings from a Vector Autoregression (VAR) model that was estimated using first-differenced data on GDP growth, inflation, and interest rates. The equation for GDP growth reveals substantial negative impacts from its own lag and from lagged inflation, suggesting contractionary effects. Inflation is notably affected by its historical values and is inversely related to interest rates, indicating the influence of monetary policy. The equation for interest rates shows a significant response to previous inflation levels, aligning with a reactive approach to monetary policy. Notably, GDP growth does not have a significant effect on either inflation or interest rates. In summary, inflation stands out as a key macroeconomic variable that influences both economic output and the behavior of monetary policy.\u003c/p\u003e\n\u003cp\u003eTable No 7 : Residual Correlation Matrix\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGDP_Growth_diff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInflation_diff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInterestRate_diff\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGDP_Growth_diff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.4650\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInflation_diff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2561\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInterestRate_diff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.4650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Compiled by author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the above table residual correlation matrix offers valuable information regarding the linear associations between the error terms of the VAR equations. A notable finding is the negative correlation (-0.4650) between GDP growth and interest rate residuals, indicating that shocks to interest rates tend to move inversely with output.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA moderate positive correlation (0.2561) exists between inflation and interest rate residuals, suggesting some shared influences or feedback mechanisms between price levels and monetary policy. The minimal correlation (0.0131) between GDP growth and inflation residuals implies limited contemporaneous association between these variables after accounting for lagged dynamics.\u003c/p\u003e\n\u003cp\u003eThis research utilizes ADF tests and VAR estimation to examine the relationships among GDP growth, inflation, and interest rates through differenced series. The ADF results indicate that GDP growth is stationary, while inflation and interest rate series require differencing. The VAR analysis reveals that GDP growth is significantly and negatively impacted by its own lag and inflation, whereas interest rates do not exhibit a significant effect. Inflation shows persistence and is adversely influenced by interest rates. Additionally, interest rates are most responsive to changes in inflation, indicating a policy reaction function. The correlations of residuals suggest a negative relationship between GDP growth and interest rates, alongside a moderate positive association between inflation and interest rates. These interactions highlight the pivotal role of inflation in influencing macroeconomic outcomes.\u003c/p\u003e\n\u003cp\u003eThe above impulse response analysis investigates the dynamic effects of a one-standard deviation shock to interest rates on GDP growth, inflation, and interest rate itself over a 10-period horizon. The findings reveal that interest rate hikes initially suppress GDP growth, which temporarily rebounds but ultimately stabilizes, with no significant long-term impact. Inflation also responds negatively in the short term, affirming the monetary policy\u0026apos;s effectiveness in curbing inflationary pressures, though the effect fades over time. Interest rates exhibit a strong immediate self-response, followed by gradual mean reversion. Overall, the analysis underscores the short-run contractionary effects of interest rate shocks and their diminishing influence over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic Checking of VAR Model for USA GDP Growth, Inflation Rate, and Interest Rate (1961\u0026ndash;2025)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable No 8: Diagnostic Results of VAR Model\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"594\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnostic Test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest Statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegrees of Freedom (df)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1. Stability (Roots of Characteristic Polynomial)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRoots: 0.4981, 0.4981, 0.4100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAll roots \u0026lt; 1 \u0026rarr; VAR is stable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2. Serial Correlation (Portmanteau Test)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e112.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo serial correlation (Fail to reject H₀)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3. Heteroskedasticity (ARCH Test)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e184.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo ARCH effect (Fail to reject H₀)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Compiled by author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiagnostic checks confirm the robustness of the estimated SVAR model. The stability test shows all characteristic roots are within the unit circle, indicating a dynamically stable system. The Portmanteau test finds no evidence of serial correlation in the residuals, and the ARCH test confirms homoskedasticity, validating model consistency. Structural impulse response analysis reveals that an interest rate shock negatively affects GDP growth and inflation in the short term, aligning with theoretical expectations of contractionary monetary policy. These responses diminish over time, highlighting the transitory nature of monetary shocks and the central role of interest rate policy in macroeconomic dynamics.\u003c/p\u003e\n\u003cp\u003eThe above graph presents the structural impulse responses of GDP growth, inflation, and interest rate to a one-standard deviation interest rate shock using a Structural VAR (SVAR) framework. The analysis spans ten periods and is underpinned by 95% bootstrap confidence intervals derived from 100 replications.\u003c/p\u003e\n\u003cp\u003eIn the immediate aftermath of a shock, there is a significant decline in GDP growth, reflecting the contractionary impact of stricter monetary policy. However, a recovery is observed in the subsequent periods, indicating a temporary positive adjustment before ultimately stabilizing at zero.\u003c/p\u003e\n\u003cp\u003eInflation also shows an immediate negative reaction, which supports the traditional monetary transmission mechanism. Although there is a slight increase after a few periods, inflation eventually stabilizes, demonstrating the effectiveness of interest rate changes in controlling inflationary pressures\u003c/p\u003e\n\u003cp\u003eIn response to its own shock, the interest rate initially surges but then gradually decreases, crossing zero around the fourth period and returning to its baseline level. This pattern illustrates mean reversion and a transient effect.\u003c/p\u003e\n\u003cp\u003eThe early responses of GDP and inflation are statistically significant, with confidence intervals not overlapping zero in initial periods. Overall, the SVAR framework provides robust and theoretically grounded insights into macroeconomic dynamics, outperforming standard VAR in capturing the causal structure of policy shocks.\u003c/p\u003e\n\u003cp\u003eSVAR Model Estimation Summary: Short-Run Structural Dynamics of GDP Growth, Inflation, and Interest Rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable No 9: SVAR Model Summary:\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"588\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDetails\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStructural VAR (A-model)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63 observations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog Likelihood\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-346.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMaximum Likelihood / Iterative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Iterations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables Included\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGDP_Growth_diff, Inflation_diff, InterestRate_diff\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIdentification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eShort-run Restrictions (A-matrix specified)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Compiled by author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe summary of the above table presents the estimation results of a Structural Vector Autoregression (SVAR) model analyzing the interdependence among GDP growth, inflation, and interest rates in the U.S. economy. The model includes 63 observations and was estimated using the Maximum Likelihood method with short-run identifying restrictions via an A-matrix specification. The log-likelihood value of -346.092 confirms a satisfactory model fit, and convergence was achieved after 11 iterations, indicating computational stability. The SVAR framework allows for clearer causal inference by imposing theoretically grounded contemporaneous restrictions, enabling a deeper understanding of structural shocks and dynamic macroeconomic relationships.\u003c/p\u003e\n\u003cp\u003eThe above graph is Forecast Error Variance Decomposition (FEVD) results from a Structural VAR model examining the dynamic interactions among GDP growth, inflation, and interest rates over a 10-period forecast horizon. The FEVD analysis quantifies the relative importance of own and cross-variable shocks in explaining the forecast error variance of each macroeconomic indicator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFor GDP Growth\u003c/strong\u003e, own shocks are the dominant drivers, contributing approximately 80%\u0026ndash;85% of its forecast error variance across all horizons. Inflation shocks explain 10%\u0026ndash;12%, while interest rate shocks have a marginal role (5%\u0026ndash;8%). This indicates that GDP growth is largely self-driven and relatively insulated from inflation and interest rate shocks in the short-to-medium term.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFor Inflation\u003c/strong\u003e, the FEVD reveals that its own shocks account for the majority of its variance (85%\u0026ndash;90%), reflecting strong persistence. Interest rate shocks contribute moderately (7%\u0026ndash;10%), consistent with the monetary policy transmission mechanism. In contrast, GDP growth shocks exert minimal influence, underscoring the weak direct impact of output on price levels in this model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFor Interest Rates\u003c/strong\u003e, about 50%\u0026ndash;55% of forecast error variance is explained by their own shocks. However, inflation shocks play a substantial role (30%\u0026ndash;35%), reflecting the Federal Reserve\u0026rsquo;s responsiveness to inflation fluctuations. GDP growth contributes a smaller portion (10%\u0026ndash;15%), suggesting output considerations have a modest but non-trivial effect on interest rate setting.\u003c/p\u003e\n\u003cp\u003eThese findings collectively highlight that GDP growth and inflation are significantly shaped by their historical trends, whereas interest rates tend to be more responsive, especially in relation to inflation. The decomposition highlights the asymmetric interdependence among the variables and provides valuable insight into the channels through which monetary policy operates in the U.S. economy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural Impact Matrix (B-Matrix) of SVAR Model: Identification Restrictions for GDP Growth, Inflation, and Interest Rate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable No 10: Summary of B Matrix:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Compiled by author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Structural Impact Matrix (B-Matrix) employed in the SVAR model adopts a diagonal identity structure to impose short-run identification restrictions. Each macroeconomic variable like GDP growth, inflation, and interest rate is assumed to respond contemporaneously only to its own structural shock, with no immediate cross-variable interactions. This recursive identification strategy, akin to a Cholesky decomposition, simplifies the causal interpretation by assigning instantaneous effects exclusively to the originating variable. Specifically, GDP growth shocks influence only output immediately, inflation shocks affect only price levels, and interest rate shocks impact only monetary policy variables contemporaneously. This structure enhances the model\u0026apos;s interpretability and isolates the independent dynamics of each variable in the short run, allowing for a clearer analysis of their structural responses over time.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study has revisited macroeconomic causality by proposing a revised Structural Vector Autoregression (SVAR) framework and applying it to the United States economy. The conventional macroeconomic model where interest rates are assumed to lead and shape inflation and GDP growth has been critically re-examined in light of significant empirical and theoretical limitations. Drawing from a comprehensive econometric analysis using ADF tests, VAR diagnostics, impulse response functions (IRFs), and forecast error variance decomposition (FEVD), this research demonstrates that GDP growth and inflation are primarily driven by real-sector and supply-side shocks, whereas interest rates function more as a delayed response mechanism.\u003c/p\u003e \u003cp\u003eThe empirical findings reinforce a reactive posture of monetary policy, particularly in the context of post-2008 monetary interventions, the COVID-19 pandemic, and the modern inflation regime influenced by global supply chains and structural imbalances. The model's revised causal ordering (Yₜ \u0026rarr; πₜ \u0026rarr; iₜ) better captures the asymmetrical, non-linear, and lagged policy responses observed in both developed and emerging economies. Moreover, the study bridges multiple theoretical paradigms Keynesian, Post-Keynesian, and elements of the New Keynesian synthesis by validating a structure where output and price levels are not simply policy-reactive but are themselves endogenous and primary drivers of economic adjustment.\u003c/p\u003e \u003cp\u003eThe global review of literature and empirical models from Japan, the EU, Sub-Saharan Africa, China, and Turkey underscores the universal relevance of this revised framework. Countries with structurally different economic setups nonetheless exhibit similar delays in monetary policy effectiveness, thereby validating the proposed SVAR model as a globally adaptable tool for macroeconomic analysis.\u003c/p\u003e \u003cp\u003eBy challenging the traditional ordering, this research contributes to a necessary evolution in structural macroeconomic modeling, offering new pathways for policy formulation and academic inquiry in a world where reactive interest rate management is the norm rather than the exception.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePolicy Recommendations\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFlexible Inflation Targeting\u003c/b\u003e: Policymakers should adopt inflation-targeting frameworks that account for supply-side and global shock drivers rather than rely solely on interest rate adjustments.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStrengthen Real-Sector Monitoring\u003c/b\u003e: Output and inflation indicators should be closely monitored as leading signals, not merely as lagged responses to interest rate policy.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eContextualize Monetary Policy by Region\u003c/b\u003e: Recognize that in EMDEs, the interest rate transmission mechanism is even weaker; fiscal and structural reforms must complement monetary actions.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eImprove International Coordination\u003c/b\u003e: In a globalized macroeconomic environment, cross-border spillovers demand that monetary authorities coordinate in their policy outlooks.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTransparency in Communication\u003c/b\u003e: Central banks should communicate not just rate changes, but also their perceived lag structures and limitations in influencing output and prices.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eInvestopedia. 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On the relative performance of inflation forecasts. \u003cem\u003eFederal Reserve Bank of St. Louis Review, 104\u003c/em\u003e(1), 13\u0026ndash;30. https://doi.org/10.20955/r.104.13-30\u003c/li\u003e\n\u003cli\u003eHaschka, R. E. (2024). Examining the new Keynesian Phillips curve in the U.S.: Why has the relationship between inflation and unemployment weakened? \u003cem\u003eResearch in Economics, 78\u003c/em\u003e(4), 101040. https://doi.org/10.1016/j.rie.2024.101040\u003c/li\u003e\n\u003cli\u003eBinder, C., \u0026amp; Kamdar, R. (2022). Expected and realized inflation in historical perspective. \u003cem\u003eJournal of Economic Perspectives, 36\u003c/em\u003e(3), 131\u0026ndash;156. https://doi.org/10.1257/jep.36.3.131\u003c/li\u003e\n\u003cli\u003eChen, K., \u0026amp; Kim, J. (2024). Two sided mirror: An analysis of inflation\u0026rsquo;s dual impact on China\u0026rsquo;s economic growth. \u003cem\u003eEast Asian Economic Review, 28\u003c/em\u003e(2), 175\u0026ndash;219. https://doi.org/10.11644/KIEP.EAER.2024.28.2.410\u003c/li\u003e\n\u003cli\u003eLaBelle, J., \u0026amp; Santacreu, A. M. (2022). Global supply chain disruptions and inflation during the COVID-19 pandemic. \u003cem\u003eFederal Reserve Bank of St. Louis Review, 104\u003c/em\u003e(2), 78\u0026ndash;91. https://doi.org/10.20955/r.104.78-91\u003c/li\u003e\n\u003cli\u003eAhmed, R., Chen, X. H., Kumpamool, C., \u0026amp; Nguyen, D. T. K. (2023). Inflation, oil prices, and economic activity in recent crisis: Evidence from the UK. \u003cem\u003eEnergy Economics, 126\u003c/em\u003e, 106918. https://doi.org/10.1016/j.eneco.2023.106918\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Graphs","content":"\u003cp\u003eGraphs 1-3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Structural Vector Autoregression (SVAR), Macroeconomic Causality, GDP Growth, Inflation Dynamics, Interest Rate Policy, Monetary Transmission Mechanism, Impulse Response Function (IRF), Forecast Error Variance Decomposition (FEVD), Time Series Econometrics, Reactive Monetary Policy","lastPublishedDoi":"10.21203/rs.3.rs-6720708/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6720708/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBy presenting a new Structural Vector Autoregression (SVAR) Model that questions the traditional ordering of interest rates, inflation, and GDP growth, this study examines macroeconomic causality. Interest rates are frequently portrayed in traditional macroeconomic models as proactive policy instruments that affect prices and output. However, the present research highlights the reactive nature of modern monetary policy by putting forth an alternative causal structure that draws on post-2008 and post-pandemic global economic disruptions. In this structure, real and supply-side shocks drive GDP growth and inflation simultaneously, while interest rates react later.\u003c/p\u003e\n\u003cp\u003eUtilizing annual data from 1961 to 2025 in the United States, the study uses rigorous time-series techniques such as forecast error variance decomposition (FEVD), impulse response functions (IRFs), VAR diagnostics, and Augmented Dickey-Fuller (ADF) tests. The findings show that inflation dominates monetary policy response, GDP growth shows considerable self-dependence, and interest rates have a limited and delayed short-term impact on macroeconomic variables.\u003c/p\u003e\n\u003cp\u003eWhile the empirical foundation is U.S.-specific, the findings have significant global implications. In an era of inflation persistence, geopolitical shocks, and central banks operating near the lower bound, this revised SVAR Model offers a globally adaptable approach for understanding dynamic macroeconomic interactions. The study contributes to the evolving literature on monetary transmission by reordering causality in a way that better reflects contemporary macroeconomic realities.\u003c/p\u003e","manuscriptTitle":"Rethinking Global Macroeconomic Causality: A Structural VAR Model Based on U.S. Evidence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-26 06:17:45","doi":"10.21203/rs.3.rs-6720708/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 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49032035,"name":"Other Economics"}],"tags":[],"updatedAt":"2025-05-26T06:17:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-26 06:17:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6720708","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6720708","identity":"rs-6720708","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}