Priors and Posterior Computation in Linear Endogenous Variable Models with Imperfect Instruments

Chan, JCC; Tobias, JL

Priors and Posterior Computation in Linear Endogenous Variable Models with Imperfect Instruments

Chan, JCC

Tobias, JL

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Publication Type:: Journal Article
Citation:: Journal of Applied Econometrics, 2015, 30 (4), pp. 650 - 674
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Chan, JCC https://orcid.org/0000-0003-3632-128X	en_US
dc.contributor.author	Tobias, JL	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Journal of Applied Econometrics, 2015, 30 (4), pp. 650 - 674	en_US
dc.identifier.issn	0883-7252	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119025
dc.description.abstract	© 2014 John Wiley & Sons, Ltd. In this paper we, like several studies in the recent literature, employ a Bayesian approach to estimation and inference in models with endogeneity concerns by imposing weaker prior assumptions than complete excludability. When allowing for instrument imperfection of this type, the model is only partially identified, and as a consequence standard estimates obtained from the Gibbs simulations can be unacceptably imprecise. We thus describe a substantially improved 'semi-analytic' method for calculating parameter marginal posteriors of interest that only require use of the well-mixing simulations associated with the identifiable model parameters and the form of the conditional prior. Our methods are also applied in an illustrative application involving the impact of body mass index on earnings.	en_US
dc.relation.ispartof	Journal of Applied Econometrics	en_US
dc.relation.isbasedon	10.1002/jae.2390	en_US
dc.subject.classification	Econometrics	en_US
dc.title	Priors and Posterior Computation in Linear Endogenous Variable Models with Imperfect Instruments	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	30	en_US
utslib.for	1402 Applied Economics	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.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	30	en_US

Abstract:

© 2014 John Wiley & Sons, Ltd. In this paper we, like several studies in the recent literature, employ a Bayesian approach to estimation and inference in models with endogeneity concerns by imposing weaker prior assumptions than complete excludability. When allowing for instrument imperfection of this type, the model is only partially identified, and as a consequence standard estimates obtained from the Gibbs simulations can be unacceptably imprecise. We thus describe a substantially improved 'semi-analytic' method for calculating parameter marginal posteriors of interest that only require use of the well-mixing simulations associated with the identifiable model parameters and the form of the conditional prior. Our methods are also applied in an illustrative application involving the impact of body mass index on earnings.

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http://hdl.handle.net/10453/119025