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

Munehisa Kasuya and Tomoki Tanemura
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Munehisa Kasuya: Bank of Japan
Tomoki Tanemura: Bank of Japan

Bank of Japan Working Paper Series from Bank of Japan

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 (search for similar items in EconPapers)
JEL-codes: C51 C52 E17 (search for similar items in EconPapers)
Date: 2000-02
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Citations: View citations in EconPapers (3)

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