Scholarlink

<p>Since the 1960s, the U.S. economy has experienced several major events, including the oil crises, the 1987 stock market crash, the global financial crisis of 2008, and the COVID-19 pandemic, which have had profound effects on market confidence. At the same time, market confidence feeds back into economic activity, creating a dynamic interaction between confidence and macroeconomic outcomes. Motivated by this mechanism, this study incorporates market confidence into a traditional monetary policy framework using a time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR-SV) to examine how confidence interacts with key macroeconomic variables. To assess the robustness of the results, we further employ a mixture innovation approach to identify the frequency of structural changes and use both the marginal likelihood and the deviance information criterion (DIC) to compare alternative VAR specifications. The model comparison results indicate that adding stochastic volatility to time-invariant VAR substantially improves model fit, while additional gains from time-varying coefficients are relatively limited. Overall, our findings suggest that market confidence plays a crucial role in shaping the transmission of monetary policy, particularly during periods of heightened uncertainty. Volatility dynamics rather than parameter drift are the primary drivers of time variation in U.S. macroeconomic fluctuations.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The Role of Market Confidence in U.S. Economic Fluctuations: Bayesian Model Comparison in an Extended TVP-VAR-SV Framework

  • 期刊论文

  • 发表日期：

    2026年5月11日

查看全文
  • Ruicheng Liang

摘要

Since the 1960s, the U.S. economy has experienced several major events, including the oil crises, the 1987 stock market crash, the global financial crisis of 2008, and the COVID-19 pandemic, which have had profound effects on market confidence. At the same time, market confidence feeds back into economic activity, creating a dynamic interaction between confidence and macroeconomic outcomes. Motivated by this mechanism, this study incorporates market confidence into a traditional monetary policy framework using a time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR-SV) to examine how confidence interacts with key macroeconomic variables. To assess the robustness of the results, we further employ a mixture innovation approach to identify the frequency of structural changes and use both the marginal likelihood and the deviance information criterion (DIC) to compare alternative VAR specifications. The model comparison results indicate that adding stochastic volatility to time-invariant VAR substantially improves model fit, while additional gains from time-varying coefficients are relatively limited. Overall, our findings suggest that market confidence plays a crucial role in shaping the transmission of monetary policy, particularly during periods of heightened uncertainty. Volatility dynamics rather than parameter drift are the primary drivers of time variation in U.S. macroeconomic fluctuations.