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4. GMM quantile regression

Type:	Artigo Científico
Title:	GMM quantile regression
Author:	Firpo, Sergio Pinheiro Galvao, Antonio F. Pinto, Cristine Campos de Xavier Poirier, Alexandre Sanroman, Graciela
Publication Date:	2022
Abstract:	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.
Keywords (english terms):	Quantile regression Generalized method of moments
Language:	Inglês
CNPq Area:	Ciências Exatas e da Terra
URI:	https://www.sciencedirect.com/science/article/pii/S0304407621001299?via%3Dihub
Copyright:	O INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DO USUÁRIO VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR.
Notes:	Texto Completo
Appears in Collections:	Coleção de Artigos Científicos

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