A New SVAR Model Based on Standardized Standard Asymmetric Exponential Power Distribution

This paper introduces a novel Structural Vector Autoregressive (SVAR) model incorporating non-Normal marginal errors described by the Standardized Standard Asymmetric Exponential Power Distribution (SSAEPD). This innovation allows the model to effectively capture the inherent skewness and kurtosis typically observed in financial data. Utilizing Markov Chain Monte Carlo (MCMC) techniques for simulating error terms and Maximum Likelihood Estimation (MLE) for parameter estimation in MATLAB, the model's robustness is significantly enhanced. We employ Impulse-Response Function (IRF) and Variance Decomposition (VD) for structural analysis to examine the relevance of classic Keynesian theory in today's US economic context. Our empirical findings suggest that the SSAEPD-enhanced SVAR model not only outperforms traditional SVAR models in capturing data characteristics but also aligns more closely with Keynesian principles. The results underscore the continued applicability of the IS-LM framework in understanding contemporary economic dynamics, demonstrating the model's superior performance in variance decomposition and structural analysis.