Luciano de Castro, Antonio F. Galvao, David M. Kaplan, Xin Liu
arXiv 11 Jul 2017 · Mathematics — Statistics Theory · publishedJournal of Econometrics (2019) · 42 citations (OpenAlex)
arXiv:1707.03436 · PDF · DOI · OpenAlex · Extracted main text
This paper develops theory for feasible estimators of finite-dimensional parameters identified by general conditional quantile restrictions, under much weaker assumptions than previously seen in the literature. This includes instrumental variables nonlinear quantile regression as a special case. More specifically, we consider a set of unconditional moments implied by the conditional quantile restrictions, providing conditions for local identification. Since estimators based on the sample moments are generally impossible to compute numerically in practice, we study feasible estimators based on smoothed sample moments. We propose a method of moments estimator for exactly identified models, as well as a generalized method of moments estimator for over-identified models. We establish consistency and asymptotic normality of both estimators under general conditions that allow for weakly dependent data and nonlinear structural models. Simulations illustrate the finite-sample properties of the methods. Our in-depth empirical application concerns the consumption Euler equation derived from quantile utility maximization. Advantages of the quantile Euler equation include robustness to fat tails, decoupling of risk attitude from the elasticity of intertemporal substitution, and log-linearization without any approximation error. For the four countries we examine, the quantile estimates of discount factor and elasticity of intertemporal substitution are economically reasonable for a range of quantiles above the median, even when two-stage least squares estimates are not reasonable.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chernozhukov, V. and C. Hansen (2006) Instrumental Quantile Regression Inference for Structural and Treatment Effect Models | 1.000 | 7 | 3 | 100% |
| 2 | Chernozhukov, V. and C. Hansen (2005) An IV Model of Quantile Treatment Effects | 1.000 | 6 | 3 | 100% |
| 3 | Andrews, D. W. K (1991) Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation | 0.928 | 4 | 3 | 100% |
| 4 | Kaplan, D. M. and Y. Sun (2017) Smoothed Estimating Equations for Instrumental Variables Quantile Regression self | 0.888 | 20 | 8 | 70% |
| 5 | Yogo, M (2004) Estimating the Elasticity of Intertemporal Substitution When Instruments are Weak | 0.874 | 7 | 2 | 100% |
| 6 | de Castro, L. and A. F. Galvao (2017) Dynamic Quantile Models of Rational Behavior self | 0.874 | 7 | 2 | 100% |
| 7 | Hall, R. E (1988) Intertemporal Substitution in Consumption | 0.874 | 5 | 2 | 100% |
| 8 | Newey, W. K. and D. McFadden (1994) Large Sample Estimation and Hypothesis Testing | 0.836 | 12 | 5 | 58% |
| 9 | Chernozhukov, V. and H. Hong (2003) An MCMC Approach to Classical Estimation | 0.811 | 4 | 2 | 100% |
| 10 | Chen, X., V. Chernozhukov, S. Lee, and W. K. Newey (2014) Local Identification of Nonparametric and Semiparametric Models | 0.737 | 3 | 3 | 67% |
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