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

Prof. Jeffrey Racine, Dept. of Economics, McMaster University, is the speaker.


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).

MacKinnon Bldg Room 224