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Nonparametric Estimation and Inference for Panel Data Models

Christopher Parmeter and Jeffrey Racine ()

Department of Economics Working Papers from McMaster University

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.

New Economics Papers: this item is included in nep-ecm
Date: 2018-01
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