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Nonparametric estimation in economics: Bayesian and frequentist approaches
Author(s) -
Chan Joshua C. C.,
Henderson Daniel J.,
Parmeter Christopher F.,
Tobias Justin L.
Publication year - 2017
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1406
Subject(s) - frequentist inference , nonparametric regression , nonparametric statistics , dirichlet process , kernel density estimation , bayesian probability , econometrics , mathematics , statistics , computer science , multivariate kernel density estimation , prior probability , bayesian inference , kernel method , artificial intelligence , variable kernel density estimation , estimator , support vector machine
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 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Statistical and Graphical Methods of Data Analysis > Density Estimation Statistical and Graphical Methods of Data Analysis > Nonparametric Methods