Bagging Time Series Models

39 Pages Posted: 5 May 2004

See all articles by Atsushi Inoue

Atsushi Inoue

Southern Methodist University

Lutz Kilian

University of Michigan at Ann Arbor - Department of Economics; Centre for Economic Policy Research (CEPR)

Date Written: March 2004

Abstract

A common problem in out-of-sample prediction is that there are potentially many relevant predictors that individually have only weak explanatory power. We propose bootstrap aggregation of pre-test predictors (or bagging for short) as a means of constructing forecasts from multiple regression models with local-to-zero regression parameters and errors subject to possible serial correlation or conditional heteroskedasticity. Bagging is designed for situations in which the number of predictors (M) is moderately large relative to the sample size (T). We show how to implement bagging in the dynamic multiple regression model and provide asymptotic justification for the bagging predictor. A simulation study shows that bagging tends to produce large reductions in the out-of-sample prediction mean squared error and provides a useful alternative to forecasting from factor models when M is large, but much smaller than T. We also find that bagging indicators of real economic activity greatly reduces the prediction mean squared error of forecasts of US CPI inflation at horizons of one month and one year.

Keywords: Bootstrap aggregation, forecasting, model selection, pre-testing

JEL Classification: C22, C52, C53

Suggested Citation

Inoue, Atsushi and Kilian, Lutz, Bagging Time Series Models (March 2004). CEPR Discussion Paper No. 4333. Available at SSRN: https://ssrn.com/abstract=540262

Atsushi Inoue

Southern Methodist University ( email )

Dallas, TX 75275
United States

Lutz Kilian (Contact Author)

University of Michigan at Ann Arbor - Department of Economics ( email )

611 Tappan Street
Ann Arbor, MI 48109-1220
United States
734-764-2320 (Phone)
734-764-2769 (Fax)

Centre for Economic Policy Research (CEPR)

London
United Kingdom

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