On Bayesian analysis and computation for functions with monotonicity and curvature restrictions

McCausland, WJ

On Bayesian analysis and computation for functions with monotonicity and curvature restrictions

McCausland, WJ

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Publication Type:: Journal Article
Citation:: Journal of Econometrics, 2008, 142 (1), pp. 484 - 507
Issue Date:: 2008-01-01

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Field	Value	Language
dc.contributor.author	McCausland, WJ	en_US
dc.date.issued	2008-01-01	en_US
dc.identifier.citation	Journal of Econometrics, 2008, 142 (1), pp. 484 - 507	en_US
dc.identifier.issn	0304-4076	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15751
dc.description.abstract	Our goal is inference for shape-restricted functions. Our functional form consists of finite linear combinations of basis functions. Prior elicitation is difficult due to the irregular shape of the parameter space. We show how to elicit priors that are flexible, theoretically consistent, and proper. We demonstrate that uniform priors over coefficients imply priors over economically relevant quantities that are quite informative and give an example of a non-uniform prior that addresses this issue. We introduce simulation methods that meet challenges posed by the shape of the parameter space. We analyze data from a consumer demand experiment. © 2007 Elsevier B.V. All rights reserved.	en_US
dc.relation.ispartof	Journal of Econometrics	en_US
dc.relation.isbasedon	10.1016/j.jeconom.2007.08.018	en_US
dc.subject.classification	Econometrics	en_US
dc.title	On Bayesian analysis and computation for functions with monotonicity and curvature restrictions	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	142	en_US
utslib.for	1403 Econometrics	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1402 Applied Economics	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
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	142	en_US

Abstract:

Our goal is inference for shape-restricted functions. Our functional form consists of finite linear combinations of basis functions. Prior elicitation is difficult due to the irregular shape of the parameter space. We show how to elicit priors that are flexible, theoretically consistent, and proper. We demonstrate that uniform priors over coefficients imply priors over economically relevant quantities that are quite informative and give an example of a non-uniform prior that addresses this issue. We introduce simulation methods that meet challenges posed by the shape of the parameter space. We analyze data from a consumer demand experiment. © 2007 Elsevier B.V. All rights reserved.

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http://hdl.handle.net/10453/15751