Nonparametric Estimation and Inference for Panel Data Models
Christopher Parmeter and
Jeffrey Racine ()
Department of Economics Working Papers from McMaster University
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.
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Persistent link: https://EconPapers.repec.org/RePEc:mcm:deptwp:2018-02
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