Working Paper: CEPR ID: DP3283
Authors: Fabio Canova
Abstract: This Paper compares the forecasting performance of some leading models of inflation for the cross section of G-7 countries. We show that bivariate and trivariate models suggested by economic theory or statistical analysis are hardly better than univariate models. Phillips curve specifications fit well into this class. Significant improvements in both the MSE of the forecasts and turning point prediction are obtained with time-varying coefficients models that exploit international interdependencies. The performance of the latter class of models is independent of the sample, while it is not the case for standard specifications.
Keywords: forecasting; inflation; Markov chain Monte Carlo methods; panel VAR models
JEL Codes: C53; E31
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Phillips curve models (E19) | better forecasts (C53) |
| univariate models (C29) | worse forecasts (C53) |
| output growth/output gap models (O41) | better forecasts (C53) |
| bivariate models (C29) | sometimes exceed univariate models (C39) |
| time-varying coefficient models (C32) | improve forecast performance (C53) |
| international information (F53) | enhance forecasting accuracy (C53) |
| multivariate models (C39) | superior for medium-term forecasts (C53) |