Chapter 74 Implementing Nonparametric and Semiparametric Estimators

Hidehiko Ichimura, Petra E. Todd

Research output: Chapter in Book/Report/Conference proceedingChapter

49 Scopus citations

Abstract

This chapter reviews recent advances in nonparametric and semiparametric estimation, with an emphasis on applicability to empirical research and on resolving issues that arise in implementation. It considers techniques for estimating densities, conditional mean functions, derivatives of functions and conditional quantiles in a flexible way that imposes minimal functional form assumptions. The chapter begins by illustrating how flexible modeling methods have been applied in empirical research, drawing on recent examples of applications from labor economics, consumer demand estimation and treatment effects models. Then, key concepts in semiparametric and nonparametric modeling are introduced that do not have counterparts in parametric modeling, such as the so-called curse of dimensionality, the notion of models with an infinite number of parameters, the criteria used to define optimal convergence rates, and "dimension-free" estimators. After defining these new concepts, a large literature on nonparametric estimation is reviewed and a unifying framework presented for thinking about how different approaches relate to one another. Local polynomial estimators are discussed in detail and their distribution theory is developed. The chapter then shows how nonparametric estimators form the building blocks for many semiparametric estimators, such as estimators for average derivatives, index models, partially linear models, and additively separable models. Semiparametric methods offer a middle ground between fully nonparametric and parametric approaches. Their main advantage is that they typically achieve faster rates of convergence than fully nonparametric approaches. In many cases, they converge at the parametric rate. The second part of the chapter considers in detail two issues that are central with regard to implementing flexible modeling methods: how to select the values of smoothing parameters in an optimal way and how to implement "trimming" procedures. It also reviews newly developed techniques for deriving the distribution theory of semiparametric estimators. The chapter concludes with an overview of approximation methods that speed up the computation of nonparametric estimates and make flexible estimation feasible even in very large size samples.

Original languageEnglish (US)
Title of host publicationHandbook of Econometrics
EditorsJames Heckman, Edward Leamer
Pages5369-5468
Number of pages100
EditionSUPPL. PART B
DOIs
StatePublished - 2007
Externally publishedYes

Publication series

NameHandbook of Econometrics
NumberSUPPL. PART B
Volume6
ISSN (Print)1573-4412

Keywords

  • additively separable models
  • asymptotic distribution theory
  • average derivative estimator
  • binning algorithms
  • convergence rates
  • flexible modeling
  • index models
  • least absolute deviations estimator
  • local polynomial estimators
  • maximum score estimator
  • nonparametric estimation
  • semiparametric estimation
  • semiparametric least squares estimator
  • smoothing parameter choice
  • trimming

ASJC Scopus subject areas

  • Economics and Econometrics

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