arXiv 23 Sep 2019 · Econometrics · publishedEconometric Theory (2020) · 5 citations (OpenAlex)
arXiv:1909.10129 · PDF · DOI · OpenAlex · Extracted main text
There are many environments in econometrics which require nonseparable modeling of a structural disturbance. In a nonseparable model with endogenous regressors, key conditions are validity of instrumental variables and monotonicity of the model in a scalar unobservable variable. Under these conditions the nonseparable model is equivalent to an instrumental quantile regression model. A failure of the key conditions, however, makes instrumental quantile regression potentially inconsistent. This paper develops a methodology for testing the hypothesis whether the instrumental quantile regression model is correctly specified. Our test statistic is asymptotically normally distributed under correct specification and consistent against any alternative model. In addition, test statistics to justify the model simplification are established. Finite sample properties are examined in a Monte Carlo study and an empirical illustration is provided.
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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 | X. Chen and D. Pouzo (2012) Estimation of nonparametric conditional moment models with possibly nonsmooth generalized residuals | 0.961 | 9 | 4 | 89% |
| 2 | X. Chen and D. Pouzo (2015) Sieve Wald and QLR inferences on semi/nonparametric conditional moment models | 0.956 | 8 | 4 | 88% |
| 3 | X. Chen, V. Chernozhukov, S. Lee, and W. K. Newey (2014) Local identification of nonparametric and semiparametric models | 0.941 | 6 | 4 | 83% |
| 4 | C. Breunig (2015) Goodness-of-fit tests based on series estimators in nonparametric instrumental regression | 0.928 | 5 | 3 | 80% |
| 5 | J. L. Horowitz (2011) Applied nonparametric instrumental variables estimation | 0.811 | 4 | 2 | 100% |
| 6 | F. Dunker, J.-P. Florens, T. Hohage, J. Johannes, and E. Mammen (2014) Iterative estimation of solutions to noisy nonlinear operator equations in nonparametric instrumental regression | 0.737 | 3 | 2 | 100% |
| 7 | J. L. Horowitz and S. Lee (2007) Nonparametric instrumental variables estimation of a quantile regression model | 0.737 | 3 | 2 | 100% |
| 8 | R. A. Adams and J. J. Fournier (2003) Sobolev Spaces, volume 140 of Pure and Applied Mathematics | 0.644 | 3 | 2 | 67% |
| 9 | X. Chen and T. M. Christensen (2015) Optimal uniform convergence rates and asymptotic normality for series estimators under weak dependence and weak conditions | 0.644 | 2 | 2 | 100% |
| 10 | X. He and P. Shi (1994) Convergence rate of b-spline estimators of nonparametric conditional quantile functions | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 41 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Are Unobservables Separable? | 0.405 | 1 | 1 |
| 2 | Adaptive, Rate-Optimal Hypothesis Testing in Nonparametric IV Models | 0.405 | 1 | 1 |
| 3 | Testability of Reverse Causality Without Exogenous Variation | 0.405 | 1 | 1 |
| 4 | On Gaussian Process Priors in Nonparametric Conditional Moment Restriction Models | 0.405 | 1 | 1 |
| 5 | Generalized Bayes in Conditional Moment Restriction Models | 0.405 | 1 | 1 |