An Alternate Parameterization for Bayesian Non-parametric / Semiparametric Regression

Tobias, JL; Chan, CC

An Alternate Parameterization for Bayesian Non-parametric / Semiparametric Regression

Tobias, JL Chan, CC

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Publisher:: Jai Press Inc.
Publication Type:: Journal Article
Citation:: Advances in Econometrics, 2019, 40B pp. 47 - 64
Issue Date:: 2019-07-01

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Field	Value	Language
dc.contributor.author	Tobias, JL	en_US
dc.contributor.author	Chan, CC https://orcid.org/0000-0003-3632-128X	en_US
dc.date.accessioned	2020-04-03T21:28:29Z
dc.date.available	2020-04-03T21:28:29Z
dc.date.issued	2019-07-01	en_US
dc.identifier.citation	Advances in Econometrics, 2019, 40B pp. 47 - 64	en_US
dc.identifier.issn	0731-9053	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139793
dc.description.abstract	We present a new procedure for nonparametric Bayesian estimation of regression functions. Specifically, our method makes use of an idea described in Frühwirth-Schnatter and Wagner (2010) to impose linearity exactly (conditional upon an unobserved binary indicator), yet also permits departures from linearity while imposing smoothness of the regression curves. An advantage of this approach is that the posterior probability of linearity is essentially produced as a by-product of the procedure. We apply our methods in both generated data experiments as well as in an illustrative application involving the impact of body mass index (BMI) on labor market earnings.	en_US
dc.publisher	Jai Press Inc.	en_US
dc.relation.ispartof	Advances in Econometrics	en_US
dc.relation.isbasedon	10.1108/S0731-90532019000040B004	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Econometrics	en_US
dc.title	An Alternate Parameterization for Bayesian Non-parametric / Semiparametric Regression	en_US
dc.type	Journal Article
utslib.citation.volume	40B	en_US
utslib.for	1403 Econometrics	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	closed_access	*
pubs.consider-herdc	true	en_US
pubs.publication-status	Published	en_US
pubs.volume	40B	en_US

Abstract:

We present a new procedure for nonparametric Bayesian estimation of regression functions. Specifically, our method makes use of an idea described in Frühwirth-Schnatter and Wagner (2010) to impose linearity exactly (conditional upon an unobserved binary indicator), yet also permits departures from linearity while imposing smoothness of the regression curves. An advantage of this approach is that the posterior probability of linearity is essentially produced as a by-product of the procedure. We apply our methods in both generated data experiments as well as in an illustrative application involving the impact of body mass index (BMI) on labor market earnings.

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http://hdl.handle.net/10453/139793