Working Paper: NBER ID: w13397
Authors: Jon Faust; Jonathan H. Wright
Abstract: Many recent papers have found that atheoretical forecasting methods using many predictors give better predictions for key macroeconomic variables than various small-model methods. The practical relevance of these results is open to question, however, because these papers generally use ex post revised data not available to forecasters and because no comparison is made to best actual practice. We provide some evidence on both of these points using a new large dataset of vintage data synchronized with the Fed's Greenbook forecast. This dataset consists of a large number of variables, as observed at the time of each Greenbook forecast since 1979. Thus, we can compare real-time large dataset predictions to both simple univariate methods and to the Greenbook forecast. For inflation we find that univariate methods are dominated by the best atheoretical large dataset methods and that these, in turn, are dominated by Greenbook. For GDP growth, in contrast, we find that once one takes account of Greenbook's advantage in evaluating the current state of the economy, neither large dataset methods nor the Greenbook process offers much advantage over a univariate autoregressive forecast.
Keywords: forecasting; greenbook; macroeconomic variables; real-time data
JEL Codes: C32; C53; E32; E37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| greenbook forecast (G) (E17) | atheoretical methods (A) (C90) |
| large dataset methods (LD) (C55) | univariate autoregressive forecasts (U) (C29) |
| greenbook forecast (G) (E17) | output growth (U) (E23) |