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Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications

Jouchi Nakajima

No 11-E-09, IMES Discussion Paper Series from Institute for Monetary and Economic Studies, Bank of Japan

Abstract: This paper aims to provide a comprehensive overview of the estimation methodology for the time-varying parameter structural vector autoregression (TVP-VAR) with stochastic volatility, in both methodology and empirical applications. The TVP-VAR model, combined with stochastic volatility, enables us to capture possible changes in underlying structure of the economy in a flexible and robust manner. In that respect, as shown in simulation exercises in the paper, the incorporation of stochastic volatility to the TVP estimation significantly improves estimation performance. The Markov chain Monte Carlo (MCMC) method is employed for the estimation of the TVP-VAR models with stochastic volatility. As an example of empirical application, the TVP-VAR model with stochastic volatility is estimated using the Japanese data with significant structural changes in dynamic relationship between the macroeconomic variables.

Keywords: Bayesian inference; Markov chain Monte Carlo; Monetary policy; State space model; Structural vector autoregression; Stochastic volatility; Time-varying parameter (search for similar items in EconPapers)
JEL-codes: C11 C15 E52 (search for similar items in EconPapers)
Date: 2011-03
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-ets and nep-ore
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