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An instrumental variable random-coefficients model for binary outcomes

In this paper, we study a random-coefficients model for a binary outcome. We allow for the possibility that some or even all of the explanatory variables are arbitrarily correlated with the random coefficients, thus permitting endogeneity. We assume the existence of observed instrumental variables Z...

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Detalles Bibliográficos
Autores principales: Chesher, Andrew, Rosen, Adam M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BlackWell Publishing Ltd 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4362209/
https://www.ncbi.nlm.nih.gov/pubmed/25798048
http://dx.doi.org/10.1111/ectj.12018
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author Chesher, Andrew
Rosen, Adam M
author_facet Chesher, Andrew
Rosen, Adam M
author_sort Chesher, Andrew
collection PubMed
description In this paper, we study a random-coefficients model for a binary outcome. We allow for the possibility that some or even all of the explanatory variables are arbitrarily correlated with the random coefficients, thus permitting endogeneity. We assume the existence of observed instrumental variables Z that are jointly independent with the random coefficients, although we place no structure on the joint determination of the endogenous variable X and instruments Z, as would be required for a control function approach. The model fits within the spectrum of generalized instrumental variable models, and we thus apply identification results from our previous studies of such models to the present context, demonstrating their use. Specifically, we characterize the identified set for the distribution of random coefficients in the binary response model with endogeneity via a collection of conditional moment inequalities, and we investigate the structure of these sets by way of numerical illustration.
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spelling pubmed-43622092015-03-18 An instrumental variable random-coefficients model for binary outcomes Chesher, Andrew Rosen, Adam M Econom J Articles In this paper, we study a random-coefficients model for a binary outcome. We allow for the possibility that some or even all of the explanatory variables are arbitrarily correlated with the random coefficients, thus permitting endogeneity. We assume the existence of observed instrumental variables Z that are jointly independent with the random coefficients, although we place no structure on the joint determination of the endogenous variable X and instruments Z, as would be required for a control function approach. The model fits within the spectrum of generalized instrumental variable models, and we thus apply identification results from our previous studies of such models to the present context, demonstrating their use. Specifically, we characterize the identified set for the distribution of random coefficients in the binary response model with endogeneity via a collection of conditional moment inequalities, and we investigate the structure of these sets by way of numerical illustration. BlackWell Publishing Ltd 2014-06 2014-04-22 /pmc/articles/PMC4362209/ /pubmed/25798048 http://dx.doi.org/10.1111/ectj.12018 Text en Copyright © 2014 Royal Economic Society http://creativecommons.org/licenses/by/3.0/ This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Chesher, Andrew
Rosen, Adam M
An instrumental variable random-coefficients model for binary outcomes
title An instrumental variable random-coefficients model for binary outcomes
title_full An instrumental variable random-coefficients model for binary outcomes
title_fullStr An instrumental variable random-coefficients model for binary outcomes
title_full_unstemmed An instrumental variable random-coefficients model for binary outcomes
title_short An instrumental variable random-coefficients model for binary outcomes
title_sort instrumental variable random-coefficients model for binary outcomes
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4362209/
https://www.ncbi.nlm.nih.gov/pubmed/25798048
http://dx.doi.org/10.1111/ectj.12018
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