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Density Estimation, from Nonparametric Econometrics: Theory and Practice
[Nonparametric Econometrics: Theory and Practice]

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Author Info

  • Qi Li

    (Texas A&M University)

  • Jeffrey Scott Racine

    (McMaster University)

Abstract

Until recently, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. Nonparametric Econometrics fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers. Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data--nominal and ordinal--in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory. This book is tailored to the needs of applied econometricians and social scientists. Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types--continuous, nominal, and ordinal--within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables.

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Bibliographic Info

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This chapter was published in: Qi Li & Jeffrey Scott Racine , , pages , 2006.

This item is provided by Princeton University Press in its series Introductory Chapters with number 8355-1.

Handle: RePEc:pup:chapts:8355-1

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Web page: http://press.princeton.edu

Related research

Keywords: nonparametric; semiparametric; statistics; econometrics; estimators; analysis;

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Cited by:
  1. Monika Merz, 2014. "Aggregation and Labor Supply Elasticities," 2014 Meeting Papers 51, Society for Economic Dynamics.
  2. Camelia Minoiu & Sanjay Reddy, 2014. "Kernel density estimation on grouped data: the case of poverty assessment," Journal of Economic Inequality, Springer, vol. 12(2), pages 163-189, June.
  3. Manuel Hernandez & Maximo Torero, 2014. "Parametric versus nonparametric methods in risk scoring: an application to microcredit," Empirical Economics, Springer, vol. 46(3), pages 1057-1079, May.

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