Smoothed GMM for quantile models

Luciano de Castro; Antonio F. Galvao; David M. Kaplan; Xin Liu

doi:10.1016/j.jeconom.2019.04.008

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Journal article

Peer reviewed

Smoothed GMM for quantile models

Luciano de Castro, Antonio F. Galvao, David M. Kaplan and Xin Liu

Journal of econometrics, Vol.213(1), pp.121-144

11/01/2019

DOI: 10.1016/j.jeconom.2019.04.008

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https://arxiv.org/pdf/1707.03436View

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Abstract

We consider estimation of finite-dimensional parameters identified by general conditional quantile restrictions, including instrumental variables quantile regression. Within a generalized method of moments framework, moment functions are smoothed to aid both computation and precision. Consistency and asymptotic normality are established under weaker assumptions than previously seen in the literature, allowing dependent data and nonlinear structural models. Simulations illustrate the finite-sample properties. An in-depth empirical application estimates the consumption Euler equation derived from quantile utility maximization. Advantages of quantile Euler equations include robustness to fat tails, decoupling risk attitude from the elasticity of intertemporal substitution, and error-free log-linearization.

Instrumental variables

Nonlinear quantile regression

Quantile utility maximization

Details

Title: Subtitle: Smoothed GMM for quantile models
Creators: Luciano de Castro - University of Iowa
Antonio F. Galvao - University of Arizona
David M. Kaplan - University of Missouri
Xin Liu - University of Missouri
Resource Type: Journal article
Publication Details: Journal of econometrics, Vol.213(1), pp.121-144
DOI: 10.1016/j.jeconom.2019.04.008
ISSN: 0304-4076
eISSN: 1872-6895
Publisher: Elsevier B.V
Language: English
Date published: 11/01/2019
Academic Unit: Economics
Record Identifier: 9984380559102771

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