Small Scale Bayesian VAR Modeling of the Japanese Macro Economy Using the Posterior Information Criterion and Monte Carlo Experiments

February 2000
Munehisa Kasuya
Tomoki Tanemura

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Abstract

We construct Bayesian vector autoregressive (BVAR) models optimized by the Posterior Information Criterion (PIC), in which hyper-parameters are data-determined in the same way as the lag length and trend order. We also assess the performance of the selected models by one-step ahead forecasts using historical data and Monte Carlo experiments. The results suggest that the selected models have a superior performance in forecasting as compared with ordinary VAR models.

Keywords:
Bayesian vector autoregression, Posterior Information Criterion, forecasting, model selection

JEL Classification:
C51, C52, E17