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.

Pages: 39 pages
Date: 2018-01
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://socialsciences.mcmaster.ca/econ/rsrch/papers/archive/2018-02.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:mcm:deptwp:2018-02

Access Statistics for this paper

More papers in Department of Economics Working Papers from McMaster University Contact information at EDIRC.
Bibliographic data for series maintained by ().