Working Paper: NBER ID: w14322
Authors: James H. Stock; Mark W. Watson
Abstract: This paper surveys the literature since 1993 on pseudo out-of-sample evaluation of inflation forecasts in the United States and conducts an extensive empirical analysis that recapitulates and clarifies this literature using a consistent data set and methodology. The literature review and empirical results are gloomy and indicate that Phillips curve forecasts (broadly interpreted as forecasts using an activity variable) are better than other multivariate forecasts, but their performance is episodic, sometimes better than and sometimes worse than a good (not naïve) univariate benchmark. We provide some preliminary evidence characterizing successful forecasting episodes.
Keywords: Phillips Curve; Inflation Forecasts; Macroeconomic Forecasting
JEL Codes: C53; E37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Phillips curve forecasts (E37) | inflation prediction accuracy (E31) |
| choice of forecasting model (C53) | inflation prediction accuracy (E31) |
| Phillips curve forecasts (during certain periods) (E31) | outperform univariate forecasts (C53) |
| Phillips curve forecasts (during other periods) (E31) | less effective than univariate forecasts (C29) |
| economic context and time period (P17) | effectiveness of forecasting models (C53) |
| multivariate inflation forecasting model (C39) | difficult to beat the best univariate model (C52) |
| economic signals (e.g., rising unemployment) (E32) | adjust inflation expectations (E31) |