题目：Testing for Quantile Sample Selection
Daniel Gutknecht is an Associate Professor at the Faculty of Economics and Business of the Goethe University Frankfurt. His research interest is theoretical and applied (micro-)econometrics, labor and health economics. Prior to joining the Goethe University Frankfurt, he has worked in the Mannheim University as an assistant professor and Oxford University as a postdoctoral research fellow. Dr. Gutknecht has published in journals like the Journal of Econometrics, Journal of Business and Economic Statistics, among others.
This paper provides a testing approach for detecting sample selection in nonparametric conditional quantile functions. Our testing strategy consists of a two-step procedure: 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, since the differentiation of the two causes has implications for nonparametric (point) identification and estimation of the conditional quantile function(s), we suggest a second test to identify 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. Our testing procedure does not require any parametric assumptions on the selection equation, and all our results hold uniformly across quantile ranks in a compact set. We apply our procedure to test for selection in log hourly wages using UK Family Expenditure Survey data.