OPTIMAL MODEL AVERAGING OF VARYING COEFFICIENT MODELS Academic Article

abstract

• Institute of Statistical Science. All rights reserved. We consider the problem of model averaging over a set of semiparametric varying coefficient models where the varying coefficients can be functions of continuous and categorical variables. We propose a Mallows model averaging procedure that is capable of delivering model averaging estimators with solid finite-sample performance. Theoretical underpinnings are provided, finite-sample performance is assessed via Monte Carlo simulation, and an illustrative application is presented. The approach is very simple to implement in practice and R code is provided as supplementary material.

authors

• Li, Qi

published proceedings

• STATISTICA SINICA

author list (cited authors)

• Li, C., Li, Q. i., Racine, J. S., & Zhang, D.

citation count

• 6

complete list of authors

• Li, Cong||Li, Qi||Racine, Jeffrey S||Zhang, Daiqiang

publication date

• October 2018

publisher

• Statistica Sinica (Institute of Statistical Science)  Publisher

published in

• STATISTICA SINICA  Journal

keywords

• Candidate Models
• Kernel Smoothing
• Semiparametric

Digital Object Identifier (DOI)

• 10.5705/ss.202017.0034

start page

• 2795

end page

• 2809

volume

• 28

issue

• 4

URL

• http://dx.doi.org/10.5705/ss.202017.0034