Optimal Model Averaging of Varying Coefficient Models

McMaster University, Department of Economics Working Paper No. 2017-1

30 Pages Posted: 3 Feb 2017

See all articles by Cong Li

Cong Li

Shanghai University of Finance and Economics - School of Economics

Qi Li

Capital University of Economics and Business

Jeffrey Racine

Department of Economics - McMaster University

Daiqiang Zhang

Texas A&M University - Department of Economics

Date Written: January 20, 2017

Abstract

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 in an appendix.

Keywords: Kernel Smoothing

JEL Classification: C14

Suggested Citation

Li, Cong and Li, Qi and Racine, Jeffrey and Zhang, Daiqiang, Optimal Model Averaging of Varying Coefficient Models (January 20, 2017). McMaster University, Department of Economics Working Paper No. 2017-1, Available at SSRN: https://ssrn.com/abstract=2905268 or http://dx.doi.org/10.2139/ssrn.2905268

Cong Li

Shanghai University of Finance and Economics - School of Economics ( email )

777 Guoding Road
Shanghai, 200433
China

Qi Li

Capital University of Economics and Business

Beijing
China

Jeffrey Racine (Contact Author)

Department of Economics - McMaster University ( email )

Hamilton, Ontario L8S 4M4
Canada

Daiqiang Zhang

Texas A&M University - Department of Economics ( email )

5201 University Blvd.
College Station, TX 77843-4228
United States

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