Analyzing cross-validation for forecasting with structural instability
Keisuke Hirano () and
Jonathan H. Wright
Journal of Econometrics, 2022, vol. 226, issue 1, 139-154
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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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:226:y:2022:i:1:p:139-154
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