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

Ryo Hasumi, Hirokuni Iiboshi, Tatsuyoshi Matsumae, Daisuke Nakamura

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)45-68
Number of pages24
JournalJournal of Asian Economics
Volume60
DOIs
Publication statusPublished - 2019 Feb

Keywords

  • Bayesian estimation
  • Density forecast
  • Dynamic prediction pool
  • Financial friction
  • Markov Chain Monte Carlo
  • Markov-switching prediction pool
  • Optimal prediction pool

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics

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