Optimal Model Averaging of Mixed-Data Kernel-Weighted Spline Regressions
Jeffrey Racine (),
Qi Li and
Department of Economics Working Papers from McMaster University
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
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:mcm:deptwp:2018-10
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