Open Access. Powered by Scholars. Published by Universities.®
Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Institution
Articles 1 - 3 of 3
Full-Text Articles in Social and Behavioral Sciences
Censored Quantile Instrumental Variable Estimation With Stata, Victor Chernozhukov, Iván Fernández-Val, Sukjin Han, Amanda E. Kowalski
Censored Quantile Instrumental Variable Estimation With Stata, Victor Chernozhukov, Iván Fernández-Val, Sukjin Han, Amanda E. Kowalski
Cowles Foundation Discussion Papers
Many applications involve a censored dependent variable and an endogenous independent variable. Chernozhukov et al. (2015) introduced a censored quantile instrumental variable estimator (CQIV) for use in those applications, which has been applied by Kowalski (2016), among others. In this article, we introduce a Stata command, cqiv, that simplifies application of the CQIV estimator in Stata. We summarize the CQIV estimator and algorithm, we describe the use of the cqiv command, and we provide empirical examples.
A Distribution-Free Stochastic Frontier Model With Endogenous Regressors, Levent Kutlu
A Distribution-Free Stochastic Frontier Model With Endogenous Regressors, Levent Kutlu
Economics and Finance Faculty Publications and Presentations
We provide a guideline for estimating a distribution-free panel data stochastic frontier model in the presence of endogenous variables. In particular, we consider variations of the within estimator of Cornwell et al. (1990) to allow endogenous regressors.
Estimation Of A Partially Linear Regression In Triangular Systems, Xin Geng, Carlos Martins-Filho, Feng Yao
Estimation Of A Partially Linear Regression In Triangular Systems, Xin Geng, Carlos Martins-Filho, Feng Yao
Economics Faculty Working Papers Series
We propose kernel-based estimators for the components of a partially linear regression in a triangular system where endogenous regressors appear both in the linear and nonparametric components of the regression. Compared with other estimators currently available in the literature, e.g. the sieve estimators proposed in Ai and Chen (2003) or Otsu (2011), our estimators have explicit functional form and are much easier to implement. They rely on a set of assumptions introduced by Newey et al. (1999) that characterize what has become known as the “control function” approach for endogeneity in regression. We explore conditional moment restrictions that make this …