Nonparametric estimation in economics: Bayesian and frequentist approaches

Joshua C.C. Chan, Daniel J. Henderson, Christopher Parmeter, Justin L. Tobias

Research output: Contribution to journalReview article

1 Citation (Scopus)

Abstract

We review Bayesian and classical approaches to nonparametric density and regression estimation and illustrate how these techniques can be used in economic applications. On the Bayesian side, density estimation is illustrated via finite Gaussian mixtures and a Dirichlet Process Mixture Model, while nonparametric regression is handled using priors that impose smoothness. From the frequentist perspective, kernel-based nonparametric regression techniques are presented for both density and regression problems. Both approaches are illustrated using a wage dataset from the Current Population Survey. WIREs Comput Stat 2017, 9:e1406. doi: 10.1002/wics.1406. For further resources related to this article, please visit the WIREs website.

Original languageEnglish (US)
Article numbere1406
JournalWiley Interdisciplinary Reviews: Computational Statistics
Volume9
Issue number6
DOIs
StatePublished - Nov 1 2017

Fingerprint

Nonparametric Regression
Nonparametric Estimation
Economics
Nonparametric Density Estimation
Regression Estimation
Finite Mixture
Dirichlet Process
Gaussian Mixture
Wages
Density Estimation
Mixture Model
Process Model
Smoothness
Regression
kernel
Resources
Review

Keywords

  • Bayesian
  • Kernel
  • Nonparametric
  • Semiparametric

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Nonparametric estimation in economics : Bayesian and frequentist approaches. / Chan, Joshua C.C.; Henderson, Daniel J.; Parmeter, Christopher; Tobias, Justin L.

In: Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 9, No. 6, e1406, 01.11.2017.

Research output: Contribution to journalReview article

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