Does a financial accelerator improve forecasts during financial crises? Evidence from Japan with prediction-pooling methods

Ryo Hasumi, Hirokuni Iiboshi, Tatsuyoshi Matsumae and Daisuke Nakamura

Journal of Asian Economics, 2019, vol. 60, issue C, 45-68

Abstract: Using a Markov-switching prediction-pooling method (Waggoner & Zha, 2012) for density forecasts, we compare the time-varying forecasting performance of a DSGE model incorporating a financial accelerator à la Bernanke, Gertler, and Gilchrist (1999) with the frictionless model by focusing on periods of financial crisis including the so-called “bubble period” and the “lost decade” in Japan. According to our empirical results, the accelerator improves the forecasting of investment over the whole sample period, while forecasts of consumption and inflation depend on the fluctuation of an extra financial premium between the policy interest rate and the corporate loan rates. In particular, several drastic monetary policy changes might disrupt the forecasting performance of the model with the accelerator. A robustness check with a dynamic pooling method (Del Negro, Hasegawa, & Schorfheide, 2016) also supports these results.

Keywords: Density forecast; Optimal prediction pool; Markov-switching prediction pool; Dynamic prediction pool; Bayesian estimation; Markov Chain Monte Carlo; Financial friction (search for similar items in EconPapers)
JEL-codes: C32 C53 E32 E37 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:asieco:v:60:y:2019:i:c:p:45-68

DOI: 10.1016/j.asieco.2018.10.005

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