Valentina Corradi, Daniel Gutknecht
arXiv 17 Jul 2019 · Econometrics · publishedEconometrics Journal (2021)
arXiv:1907.07412 · PDF · DOI · OpenAlex · Extracted main text
This paper provides tests for detecting sample selection in nonparametric conditional quantile functions. The first test is an omitted predictor test with the propensity score as the omitted variable. As with any omnibus test, in the case of rejection we cannot distinguish between rejection due to genuine selection or to misspecification. Thus, we suggest a second test to provide supporting evidence whether the cause for rejection at the first stage was solely due to selection or not. Using only individuals with propensity score close to one, this second test relies on an `identification at infinity' argument, but accommodates cases of irregular identification. Importantly, neither of the two tests requires parametric assumptions on the selection equation nor a continuous exclusion restriction. Data-driven bandwidth procedures are proposed, and Monte Carlo evidence suggests a good finite sample performance in particular of the first test. Finally, we also derive an extension of the first test to nonparametric conditional mean functions, and apply our procedure to test for selection in log hourly wages using UK Family Expenditure Survey data as \citet{AB2017}.
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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 | Arellano, M. and S. Bonhomme (2017) Quantile selection models with an application to understanding changes in wage inequality | 1.000 | 18 | 6 | 100% |
| 2 | Li, Q. and J. Racine (2008) Nonparametric estimation of conditional cdf and quantile functions with mixed categorical and continuous data | 1.000 | 7 | 4 | 100% |
| 3 | Das, M., W. K. Newey, and F. Vella (2003) Nonparametric estimation of sample selection models | 0.843 | 5 | 3 | 60% |
| 4 | Volgushev, S., M. Birke, H. Dette, and N. Neumeyer (2013) Significance testing in quantile regression | 0.811 | 4 | 2 | 100% |
| 5 | Escanciano, J. C., D. Jacho-Chavez, and A. Lewbel (2014) Uniform convergence of weighted sums of non- and semiparametric residuals for estimation and testing | 0.754 | 7 | 4 | 43% |
| 6 | Heckman, J (1979) Sample selection bias as a specification error | 0.737 | 3 | 3 | 67% |
| 7 | Hayfield, T. and J. Racine (2008) Nonparametric econometrics: The np package | 0.737 | 3 | 3 | 67% |
| 8 | Khan, S. and E. Tamer (2010) Irregular identification, support conditions, and inverse weight estimation | 0.737 | 3 | 2 | 100% |
| 9 | Guerre, E. and C. Sabbah (2012) Uniform bias study and bahadur representation for local polynomial estimators of the conditional quantile function | 0.644 | 2 | 2 | 100% |
| 10 | Heckman, J (1974) Shadow prices, market wages and labor supply | 0.644 | 2 | 2 | 100% |
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