Optimal Model Averaging of Mixed-Data Kernel-Weighted Spline Regressions

Jeffrey Racine, Qi Li, Dalei Yu and Li Zheng

Journal of Business & Economic Statistics, 2023, vol. 41, issue 4, 1251-1261

Abstract: Model averaging has a rich history dating from its use for combining forecasts from time-series models (Bates and Granger) and presents a compelling alternative to model selection methods. We propose a frequentist model averaging procedure defined over categorical regression splines (Ma, Racine, and Yang) that allows for mixed-data predictors, as well as nonnested and heteroscedastic candidate models. We demonstrate the asymptotic optimality of the proposed model averaging estimator, and develop a post-averaging inference theory for it. Theoretical underpinnings are provided, finite-sample performance is evaluated, and an empirical illustration reveals that the method is capable of outperforming a range of popular model selection criteria in applied settings. An R package is available for practitioners (Racine).

Date: 2023
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Working Paper: Optimal Model Averaging of Mixed-Data Kernel-Weighted Spline Regressions (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:41:y:2023:i:4:p:1251-1261

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DOI: 10.1080/07350015.2022.2118126

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