How Useful is Bagging in Forecasting Economic Time Series? A Case Study of US CPI Inflation

Lutz Kilian and Atsushi Inoue

No 5304, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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: Bootstrap aggregation; Bayesian model averaging; Forecast combination; Factor models; Shrinkage estimation; Forecast model selection; Pre-testing (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 (search for similar items in EconPapers)
Date: 2005-10
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Citations: View citations in EconPapers (18)

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