Modeling and Forecasting Inflation in Japan, Issues 2001-2082
This paper estimates an inflation function and forecasts one-year ahead inflation for Japan. It finds that (i) markup relationships, excess money and the output gap are particularly relevant long-run determinants for an equilibrium correction model (EqCM) of inflation; (ii) with intercept corrections, one-year ahead inflation forecast performance of the EqCM is good; and (iii) forecast accuracy can be improved by combining forecasts of the EqCM with those made by rival models. The EqCM obtained would serve for structural model-based inflation forecasting. It also highlights the importance of adjustment to a pure model-based forecast by utilizing information of alternative models. The methodology employed is applicable to a wider range of countries including some emerging market economies.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Forecast Performances of Quarterly Model
1 other sections not shown
Other editions - View all
4-quarter ahead inflation actual outcomes ahead inflation forecast Atrace benchmark models Bias SE MSFE centered seasonal dummies Clements and Hendry cointegrating vector Combination of EqCM combining forecasts constant and centered core inflation Doornik effective exchange rate eigenvalue and trace EqCM money EqCM with money equilibrium correction error bands excess money explanatory variables fgap Figure forecast accuracy forecast biases forecast combinations Forecast Performances heteroscedasticity HP filter hpgap imfgap inflation function inflation indicators inflation process Inodel intercept corrections Japan Japanese Inflation Model Juselius long-run relationships markup relationships oil crisis one-year ahead inflation output gap overfit potential output principal component indicators pure structural model-based random walk model real effective exchange sample period second oil crisis series model series techniques single equation standard errors Stock and Watson structural model-based forecast structural time series supply shock Table trace statistics trend unit root univariate model vector autoregression Wholesale Price Index X12ARIMA