2015
DOI: 10.1016/j.jeconom.2014.06.017
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Quantile regression with censoring and endogeneity

Abstract: We thank Denis Chetverikov and Sukjin Han for excellent comments and capable research assistance. We are grateful to Richard Blundell for providing us the data for the empirical application. Stata software to implement the methods developed in the paper is available in Amanda Kowalski's web site at http://www.econ.yale.edu/ak669/research.html. We gratefully acknowledge research support from the NSF. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bur… Show more

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Cited by 119 publications
(124 citation statements)
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“…In addition, we run the quantile model suggested by Chernozhukov et al (), which applies a control function approach to control for potential endogeneity of the financing decision. After estimation of the control variable in stage one, the second stage of the procedure controls for endogeneity of VC investment by taking the control for potential endogeneity of VC investment into account.…”
Section: Sample Data and Methodologymentioning
confidence: 99%
See 1 more Smart Citation
“…In addition, we run the quantile model suggested by Chernozhukov et al (), which applies a control function approach to control for potential endogeneity of the financing decision. After estimation of the control variable in stage one, the second stage of the procedure controls for endogeneity of VC investment by taking the control for potential endogeneity of VC investment into account.…”
Section: Sample Data and Methodologymentioning
confidence: 99%
“…Using the panel quantile regression approach according to Canay (), which controls for unobserved time‐invariant characteristics, we observe that founder involvement spurs turnover growth in firms that follow trajectories of medium and high growth. This analysis is complemented by applying the control function approach of the quantile fixed effects model to control for potential endogeneity of the financing decision (Chernozhukov, Fernández‐Val, & Kowalski, , ). To reliably interpret our findings, we further apply the method of Oster (), which extends the theory of Altonji, Elder, and Taber () to assess the robustness of our results to omitted variable bias.…”
Section: Introductionmentioning
confidence: 99%
“…We tried to implement ideas from quantile regression with endogenous regressors (Chernozhukov, Fernández-Val, & Kowalski, 2015) which are similar to the ideas of 2SLS. After fixing some problems, we run the STATA code of Chernozhukov, Fernandez-Val, Han, and Kowalski (2018) but without significant results.…”
Section: Randomized Cross-validationmentioning
confidence: 99%
“…After fixing some problems, we run the STATA code of Chernozhukov, Fernandez-Val, Han, and Kowalski (2018) but without significant results. Considering the complexity of the model by Chernozhukov et al (2015), we would most likely require a much larger data set to get stable results. Moreover, their model is also not designed for panel data.…”
Section: Randomized Cross-validationmentioning
confidence: 99%
“…Additionally, we consider the procedure recently developed by Oster (, ) to estimate bounds for the treatment effect under the notion that movements in the coefficient of interest, when including and not including controls for which one is concerned about omitted variables, are informative about the remaining bias from unobservables. Finally, we use new censored quantile IV (CQIV) estimator, developed by Chernozhukov, Fernandez‐Val, and Kowalski (), to investigate if students respond differently along the conditional distribution of scores.…”
Section: Introductionmentioning
confidence: 99%