GMM quantile regression
This paper develops generalized method of moments (GMM) estimation and inference procedures for quantile regression models. We propose a GMM estimator for simultaneous estimation across multiple quantiles. This estimator allows us to model quantile regression coefficients using flexible parametric r...
| Autores: | , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | Brasil |
| Recursos: | Instituição de Ensino Superior e de Pesquisa (INSPER) |
| Repositorio: | Repositório Institucional da INSPER |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.insper.edu.br:11224/4286 |
| Acesso em linha: | https://repositorio.insper.edu.br/handle/11224/4286 https://doi.org/10.1016/j.jeconom.2020.11.014 |
| Access Level: | acceso abierto |
| Palavra-chave: | Quantile regression Generalized method of moments |
| Resumo: | This paper develops generalized method of moments (GMM) estimation and inference procedures for quantile regression models. We propose a GMM estimator for simultaneous estimation across multiple quantiles. This estimator allows us to model quantile regression coefficients using flexible parametric restrictions across quantiles. The restrictions and simultaneous estimation lead to efficiency gains compared to standard methods. We establish the asymptotic properties of the GMM estimators when the number of quantiles used is fixed and when it diverges to infinity jointly with the sample size. As an alternative to GMM, we also propose a minimum distance estimator over a given subset of quantiles. Moreover, we provide specification tests for the imposed restrictions. The estimators and tests we propose are simple to implement in practice. Monte Carlo simulations provide numerical evidence of the finite sample properties of the methods. Finally, we apply the proposed methods to estimate the effects of smoking on birthweight of live infants at the extreme bottom of the conditional distribution. |
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