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

Jeffrey Racine, Qi Li and Li Zheng

Department of Economics Working Papers from McMaster University

Abstract: Model averaging has a rich history dating from its use for combining forecasts from time-series models (Bates & Granger 1969) and presents a compelling alternative to model selection methods. We propose a frequentist model average procedure defined over categorical regression splines (Ma, Racine & Yang 2015) that allows for non-nested and heteroskedastic candidate models. 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 2017).

Pages: 37 pages
Date: 2018-05
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
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http://socialsciences.mcmaster.ca/econ/rsrch/papers/archive/2018-10.pdf (application/pdf)

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Journal Article: Optimal Model Averaging of Mixed-Data Kernel-Weighted Spline Regressions (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:mcm:deptwp:2018-10

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