Nonseparable Sample Selection Models with Censored Selection Rules
We consider identification and estimation of nonseparable sample selection models with censored selection rules. We employ a control function approach and discuss different objects of interest based on (1) local effects conditional on the control function, and (2) global effects obtained from integration over ranges of values of the control function. We derive the conditions for the identification of these different objects and suggest strategies for estimation. Moreover, we provide the associated asymptotic theory. These strategies are illustrated in an empirical investigation of the determinants of female wages in the United Kingdom.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Estimating Nonseparable Selection Models: A Functional Contraction Approach
We propose a novel method for estimating nonseparable selection models. We show that, given the selection rule and the observed selected outcome distribution, the potential outcome distribution can be characterized as th…
Distribution Regression with Censored Selection
We develop a distribution regression model with a censored selection rule, offering a semi-parametric generalization of the Heckman selection model. Our approach applies to the entire distribution, extending beyond the m…
regressionMachine Learning for Online Algorithm Selection under Censored Feedback
In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate al…
BIG-bench Machine LearningThompson SamplingSelection and the Distribution of Female Hourly Wages in the U.S
We analyze the role of selection bias in generating the changes in the observed distribution of female hourly wages in the United States using CPS data for the years 1975 to 2020. We account for the selection bias from t…
Selection biasProper Scoring Rules for Right-Censored Survival Data
Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts. However, in the presence of right censoring, the event time is only partially observed, rendering conv…