Analyzing cross-validation for forecasting with structural instability

Keisuke Hirano, Jonathan H. Wright

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)139-154
Number of pages16
JournalJournal of Econometrics
Issue number1
StatePublished - Jan 2022

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics


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