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

Atsushi Inoue, Lu Jin and Barbara Rossi

Journal of Econometrics, 2017, vol. 196, issue 1, 55-67

Abstract: There is strong evidence of structural changes in macroeconomic time series, and the forecasting performance is often sensitive to the choice of estimation window size. This paper develops a method for selecting the window size for forecasting. Our proposed method is to choose the optimal 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 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)
JEL-codes: C14 C22 C53 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (127)

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Related works:
Working Paper: Rolling window selection for out-of-sample forecasting with time-varying parameters (2016)
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:eee:econom:v:196:y:2017:i:1:p:55-67

DOI: 10.1016/j.jeconom.2016.03.006

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