Does a financial accelerator improve forecasts during financial crises?: Evidence from Japan with Prediction Pool Methods

Using a Markov-switching prediction pool method (Waggoner and Zha, 2012) in terms of density forecasts, we assess the time-varying forecasting performance of a DSGE model incorporating a financial accelerator a la Bernanke et al. (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 corporate loan rates. In particular, several drastic monetary policy changes might disrupt the forecasting performance of the model with the accelerator. A robust check with a dynamic pool method (Del Negro et al., 2016) also supports these results.