Quantile regression with an endogenous misclassified binary regressor

Recent work on the conditional mean model offers the possibility of addressing misreporting of participation in social programs, which is common and has increased in all major surveys. However, researchers who employ quantile regression continue to encounter challenges in terms of estimation and sta...

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Detalles Bibliográficos
Autor principal: Lamarche, Carlos
Formato: Articulo Documento de trabajo
Lenguaje:Inglés
Publicado: 2023
Materias:
Ciencias Económicas
Quantile regression
Misclassification
Endogenous Treatments
Survey data
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/157397
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-157397
record_format dspace
spelling I19-R120-10915-1573972023-09-07T20:07:20Z http://sedici.unlp.edu.ar/handle/10915/157397 Quantile regression with an endogenous misclassified binary regressor Lamarche, Carlos 2023-09 2023-09-07T17:30:05Z en Ciencias Económicas Quantile regression Misclassification Endogenous Treatments Survey data Recent work on the conditional mean model offers the possibility of addressing misreporting of participation in social programs, which is common and has increased in all major surveys. However, researchers who employ quantile regression continue to encounter challenges in terms of estimation and statistical inference. In this work, we propose a simple two-step estimator for a quantile regression model with endogenous misreporting. The identification of the model uses a parametric first stage and information related to participation and misreporting. We show that the estimator is consistent and asymptotically normal. We also establish that a bootstrap procedure is asymptotically valid for approximating the distribution of the estimator. Simulation studies show the small sample behavior of the estimator in comparison with other methods, including a new three-step estimator. Finally, we illustrate the novel approach using U.S. survey data to estimate the intergenerational effect of mother's participation on welfare on daughter's adult income. Centro de Estudios Distributivos, Laborales y Sociales Articulo Documento de trabajo http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) application/pdf
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Económicas
Quantile regression
Misclassification
Endogenous Treatments
Survey data
spellingShingle Ciencias Económicas
Quantile regression
Misclassification
Endogenous Treatments
Survey data
Lamarche, Carlos
Quantile regression with an endogenous misclassified binary regressor
topic_facet Ciencias Económicas
Quantile regression
Misclassification
Endogenous Treatments
Survey data
description Recent work on the conditional mean model offers the possibility of addressing misreporting of participation in social programs, which is common and has increased in all major surveys. However, researchers who employ quantile regression continue to encounter challenges in terms of estimation and statistical inference. In this work, we propose a simple two-step estimator for a quantile regression model with endogenous misreporting. The identification of the model uses a parametric first stage and information related to participation and misreporting. We show that the estimator is consistent and asymptotically normal. We also establish that a bootstrap procedure is asymptotically valid for approximating the distribution of the estimator. Simulation studies show the small sample behavior of the estimator in comparison with other methods, including a new three-step estimator. Finally, we illustrate the novel approach using U.S. survey data to estimate the intergenerational effect of mother's participation on welfare on daughter's adult income.
format Articulo
Documento de trabajo
author Lamarche, Carlos
author_facet Lamarche, Carlos
author_sort Lamarche, Carlos
title Quantile regression with an endogenous misclassified binary regressor
title_short Quantile regression with an endogenous misclassified binary regressor
title_full Quantile regression with an endogenous misclassified binary regressor
title_fullStr Quantile regression with an endogenous misclassified binary regressor
title_full_unstemmed Quantile regression with an endogenous misclassified binary regressor
title_sort quantile regression with an endogenous misclassified binary regressor
publishDate 2023
url http://sedici.unlp.edu.ar/handle/10915/157397
work_keys_str_mv AT lamarchecarlos quantileregressionwithanendogenousmisclassifiedbinaryregressor
_version_ 1807221222507806720

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