Working Paper: CEPR ID: DP5304
Authors: Atsushi Inoue; Lutz Kilian
Abstract: This paper explores the usefulness of bagging methods in forecasting economic time series from linear multiple regression models. We focus on the widely studied question of whether the inclusion of indicators of real economic activity lowers the prediction mean-squared error of forecast models of US consumer price inflation. We study bagging methods for linear regression models with correlated regressors and for factor models. We compare the accuracy of simulated out-of-sample forecasts of inflation based on these bagging methods to that of alternative forecast methods, including factor model forecasts, shrinkage estimator forecasts, combination forecasts and Bayesian model averaging. We find that bagging methods in this application are almost as accurate or more accurate than the best alternatives. Our empirical analysis demonstrates that large reductions in the prediction mean squared error are possible relative to existing methods, a result that is also suggested by the asymptotic analysis of some stylized linear multiple regression examples.
Keywords: Bayesian Model Averaging; Bootstrap Aggregation; Factor Models; Forecast Combination; Forecast Model Selection; Pretesting; Shrinkage Estimation
JEL Codes: C22; C52; C53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| inclusion of indicators of real economic activity (E01) | lower PMSE (E64) |
| bagging methods (C90) | lower PMSE (E64) |
| bagging methods (C90) | more accurate forecasts than traditional approaches (C53) |
| bagging (C78) | reduce forecast variance (C53) |
| bagging (C78) | maintain similar bias levels (C46) |
| bagging performance (C52) | robust to variations in number of predictors (C51) |
| bagging performance (C52) | robust to choice of critical values for model selection (C52) |