Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications
AbstractThis 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 this respect, as shown in simulation exercises in the paper, the incorporation of stochastic volatility into the TVP estimation significantly improves estimation performance. The Markov chain Monte Carlo 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 the dynamic relationship between the macroeconomic variables.
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Bibliographic InfoArticle provided by Institute for Monetary and Economic Studies, Bank of Japan in its journal Monetary and Economic Studies.
Volume (Year): 29 (2011)
Issue (Month): (November)
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Bayesian inference; Markov chain Monte Carlo; Monetary policy; State space model; Structural vector autoregression; Stochastic volatility; Time-varying parameter;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
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