Nonparametric estimation in economics: Bayesian and frequentist approaches

Chan, JCC; Henderson, DJ; Parmeter, CF; Tobias, JL

Nonparametric estimation in economics: Bayesian and frequentist approaches

Chan, JCC

Henderson, DJ Parmeter, CF Tobias, JL

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Publication Type:: Journal Article
Citation:: Wiley Interdisciplinary Reviews: Computational Statistics, 2017, 9 (6)
Issue Date:: 2017-11-01

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Field	Value	Language
dc.contributor.author	Chan, JCC https://orcid.org/0000-0003-3632-128X	en_US
dc.contributor.author	Henderson, DJ	en_US
dc.contributor.author	Parmeter, CF	en_US
dc.contributor.author	Tobias, JL	en_US
dc.date.available	2020-05-25T19:02:51Z
dc.date.issued	2017-11-01	en_US
dc.identifier.citation	Wiley Interdisciplinary Reviews: Computational Statistics, 2017, 9 (6)	en_US
dc.identifier.issn	1939-5108	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124348
dc.description.abstract	© 2017 Wiley Periodicals, Inc. 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.	en_US
dc.relation.ispartof	Wiley Interdisciplinary Reviews: Computational Statistics	en_US
dc.relation.isbasedon	10.1002/wics.1406	en_US
dc.title	Nonparametric estimation in economics: Bayesian and frequentist approaches	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	9	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0104 Statistics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Economics Discipline
utslib.copyright.status	open_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	9	en_US

Abstract:

© 2017 Wiley Periodicals, Inc. 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.

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http://hdl.handle.net/10453/124348