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.
© This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
|Work Title||Analyzing Cross-Validation for Forecasting with Model Uncertainty|
|License||CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)|
|Publication Date||January 1, 2022|
|Publisher Identifier (DOI)||
|Deposited||February 17, 2023|
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