 Identification and estimation of a heteroskedastic censored regression model with random coefficient dummy endogenous regressorsJing Guo, Lei Wang and Zhengyu Zhang Economic Modelling, 2022, vol. 110, issue C Abstract: Censoring and the impact of interventions to differ across treated units are two common phenomena in applied micro-econometrics. This paper studies identification and estimation of a heteroskedastic censored regression model with random coefficient dummy regressors. This approach imposes no parametric distributional assumption on error terms and allows for a purely nonparametric participation decision equation. The resulting estimator is shown to be n-consistent and asymptotically normal. Moreover, our estimation approach can readily extend to a censored regression model with multiple random coefficient dummy endogenous regressors. A Monte Carlo simulation shows that our estimator performs reasonably well for finite samples. An application to evaluate the effect of fertility on female labor supply is provided, and the result indicates that the presence of a third child causes work hours per week to decline by approximately 2.5 h. Keywords: Censoring; Heteroskedasticity; Random coefficient; Partially linear varying coefficient model; Distribution-free (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:110:y:2022:i:c:s0264999322000451 DOI: 10.1016/j.econmod.2022.105799 Access Statistics for this article Economic Modelling is currently edited by S. Hall and P. Pauly More articles in Economic Modelling from Elsevier |
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