Analyzing Cross-Validation for Forecasting with Model Uncertainty

When forecasting with economic time series data, researchers often use a restricted window of observations or downweight past observations in order to mitigate the potential effects of parameter instability. In this paper, we study the problem of selecting a window for point forecasts made at the end of the sample. We develop asymptotic approximations to the sampling properties of window selection methods, and post-window selection point forecasts, where there is local parameter instability of various sorts. We examine risk properties of point forecasts made after cross-validation to select the window, and compare this approach to some alternative methods of selecting the window. We also propose a quasi-Bayesian form of cross-validation that we find to have good risk properties.

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Work Title Analyzing Cross-Validation for Forecasting with Model Uncertainty
Open Access
  1. Keisuke Hirano
  2. Jonathan H Wright
License CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)
Work Type Article
  1. Journal of Econometrics
Publication Date January 1, 2022
Publisher Identifier (DOI)
Deposited February 17, 2023




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  • Added Creator Keisuke Hirano
  • Added Creator Jonathan H Wright
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