Smoothed GMM for quantile models

Luciano de Castro, Antonio Galvao, David Kaplan and Xin Liu

Journal of Econometrics, 2019, vol. 213, issue 1, 121-144

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.

Keywords: Instrumental variables; Nonlinear quantile regression; Quantile utility maximization (search for similar items in EconPapers)
JEL-codes: C31 C32 C36 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (27)

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Related works:
Working Paper: Smoothed GMM for quantile models (2018)
Working Paper: Smoothed instrumental variables quantile regression, with estimation of quantile Euler equations (2018)
Working Paper: Smoothed GMM for quantile models (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:213:y:2019:i:1:p:121-144

DOI: 10.1016/j.jeconom.2019.04.008

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