Nonparametric Estimation and Inference for Panel Data Models

McMaster University - Department of Economics Working Paper No. 2018-02

39 Pages Posted: 10 Jan 2018

See all articles by Christopher Parmeter

Christopher Parmeter

University of Miami

Jeffrey Racine

Department of Economics - McMaster University

Date Written: January 2, 2018

Abstract

This chapter surveys nonparametric methods for estimation and inference in a panel data setting. Methods surveyed include profile likelihood, kernel smoothers, as well as series and sieve estimators. The practical application of nonparametric panel-based techniques is less prevalent that, say, nonparametric density and regression techniques. It is our hope that the material covered in this chapter will prove useful and facilitate their adoption by practitioners.

JEL Classification: C14

Suggested Citation

Parmeter, Christopher and Racine, Jeffrey, Nonparametric Estimation and Inference for Panel Data Models (January 2, 2018). McMaster University - Department of Economics Working Paper No. 2018-02, Available at SSRN: https://ssrn.com/abstract=3097813 or http://dx.doi.org/10.2139/ssrn.3097813

Christopher Parmeter

University of Miami ( email )

Coral Gables, FL 33124
United States

Jeffrey Racine (Contact Author)

Department of Economics - McMaster University ( email )

Hamilton, Ontario L8S 4M4
Canada

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