Rolling window selection for out-of-sample forecasting with time-varying parameters

Atsushi Inoue, Lu Jin and Barbara Rossi

Economics Working Papers from Department of Economics and Business, Universitat Pompeu Fabra

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 2018, 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, especially for output growth.

Keywords: Macroeconomic forecasting; parameter instability; nonparametric estimation; bandwidth selection. (search for similar items in EconPapers)
Date: 2014-06, Revised 2016-04
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Rolling window selection for out-of-sample forecasting with time-varying parameters (2017)
Working Paper: Rolling Window Selection for Out-of-Sample Forecasting with Time-Varying Parameters (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:upf:upfgen:1435

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