Bayesian Analysis of Time-Varying Parameter Vector Autoregressive Model for the Japanese Economy and Monetary Policy
This paper analyzes the time-varying parameter vector autoregressive (TVP-VAR) model for the Japanese economy and monetary policy. The time-varying parameters are estimated via the Markov chain Monte Carlo method and the posterior estimates of parameters reveal the time-varying structure of the Japanese economy and monetary policy during the period from 1981 to 2008. The marginal likelihoods of the TVP-VAR model and other VAR models are also estimated. The estimated marginal likelihoods indicate that the TVP-VAR model best fits the Japanese economic data.
Bayesian inference, Markov chain Monte Carlo, Monetary policy, State space model, Structural vector autoregressive model, Stochastic volatility, Time-varying parameter
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