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Smoothed GMM for quantile models

Author:
de Castro, Luciano, Galvao, Antonio F., Kaplan, David M., Liu, Xin
Source:
Journal of econometrics 2019
ISSN:
0304-4076
Subject:
econometric models, economic analysis, economic theory, equations, regression analysis, risk
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.
Agid:
6393739