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

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  • Jeffrey S. Racine
  • Qi Li
  • Li Zheng

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).

Suggested Citation

  • Jeffrey S. Racine & Qi Li & Li Zheng, 2018. "Optimal Model Averaging of Mixed-Data Kernel-Weighted Spline Regressions," Department of Economics Working Papers 2018-10, McMaster University.
    • Handle: RePEc:mcm:deptwp:2018-10
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    References listed on IDEAS

    as
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