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Bagging Time Series Models

Lutz Kilian () and Atsushi Inoue ()

No 110, Econometric Society 2004 North American Summer Meetings from Econometric Society

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 redcues the prediction mean squared error of forecasts of U.S. CPI inflation at horizons of one month and one year

Keywords: forecasting; bootstrap; model selection; pre-testing; forecast aggregation; factor models; inflation. (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 (search for similar items in EconPapers)
Date: 2004-08-11
New Economics Papers: this item is included in nep-ets
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