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Rolling Window Selection for Out-of-Sample Forecasting with Time-Varying Parameters

Atsushi Inoue (), Lu Jin and Barbara Rossi ()

No 768, Working Papers from Barcelona Graduate School of Economics

Abstract: While forecasting is a common practice in academia, government and business alike, practitioners are often left wondering how to choose the sample for estimating forecasting models. When we forecast inflation in 2014, for example, should we use the last 30 years of data or the last 10 years of data? There is strong evidence of structural changes in economic time series, and the forecasting performance is often quite sensitive to the choice of such window size". In this paper, we develop a novel method for selecting the estimation window size for forecasting. Specifically, we propose to choose the optimal window size that minimizes the forecaster's quadratic loss function, and we prove the asymptotic validity of our approach. Our Monte Carlo experiments show that our method performs quite well under various types of structural changes. When applied to forecasting US real output growth and inflation, the proposed method tends to improve upon conventional methods. Forthcoming in Journal of Econometrics

Keywords: macroeconomic forecasting; parameter instability; nonparametric estimation; bandwidth selection (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cna, nep-ino, nep-lma, nep-ltv and nep-opm
Date: 2014-06
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Related works:
Journal Article: Rolling window selection for out-of-sample forecasting with time-varying parameters (2017) Downloads
Working Paper: Rolling window selection for out-of-sample forecasting with time-varying parameters (2016) Downloads
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