Nonparametric estimation of marginal effects in regression-spline random effects models

Abstract

We consider a B-spline regression approach toward nonparametric modeling of a random effects (error component) model. We focus our attention on the estimation of marginal effects (derivatives) and their asymptotic properties. Theoretical underpinnings are provided, finite-sample performance is evaluated via Monte–Carlo simulation, and an application that examines the contribution of different types of public infrastructure on private production is investigated using panel data comprising the 48 contiguous states in the United States over the period 1970–1986.

Keywords:

• B-spline
• consistent
• GLS

JEL Classification:

• C14
• C23

Acknowledgments

We would like to thank John Kealey for his helpful comments and feedback, and Wei Lin for discussions on the subject matter of this article. We are also grateful to the editor and two referees for their helpful comments.

Additional information

Funding

Ullah gratefully acknowledges research support from the academic senate, UCR. Racine would like to gratefully acknowledge support from the Social Sciences and Humanities Research Council of Canada (SSHRC:www.sshrc.ca), and the Shared Hierarchical Academic Research Computing Network (SHARCNET:www.sharcnet.ca). Ma's research was partially supported by National ScienceFoundation grants DMS 1306972 and 1712558.