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Author: Lee, Sokbae

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1.	Carneiro, Pedro M. Lee, Sokbae
1.	Estimating Distributions of Potential Outcomes Using Local Instrumental Variables with an Application to Changes in College Enrollment and Wage Inequality Journal of Econometrics 149,2 (April 2009): 191-208. Also: http://www.sciencedirect.com/science/article/pii/S0304407609000281 Cohort(s): NLSY79 Publisher: Elsevier Keyword(s): College Enrollment; Colleges; High School Completion/Graduates; Schooling; Variables, Instrumental; Wage Equations; Wages This paper extends the method of local instrumental variables developed by Heckman and Vytlacil [Heckman, J., Vytlacil E., 2005. Structural equations, treatment, effects and econometric policy evaluation. Econometrica 73(3), 669–738] to the estimation of not only means, but also distributions of potential outcomes. The newly developed method is illustrated by applying it to changes in college enrollment and wage inequality using data from the National Longitudinal Survey of Youth of 1979. Increases in college enrollment cause changes in the distribution of ability among college and high school graduates. This paper estimates a semiparametric selection model of schooling and wages to show that, for fixed skill prices, a 14% increase in college participation (analogous to the increase observed in the 1980s), reduces the college premium by 12% and increases the 90–10 percentile ratio among college graduates by 2%. [Copyright 2009 Elsevier] Copyright of Journal of Econometrics is the property of Elsevier Science Publishers B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts)
	Bibliography Citation Carneiro, Pedro M. and Sokbae Lee. "Estimating Distributions of Potential Outcomes Using Local Instrumental Variables with an Application to Changes in College Enrollment and Wage Inequality." Journal of Econometrics 149,2 (April 2009): 191-208.

2.	Horowitz, Joel L. Lee, Sokbae
2.	Semiparametric Estimation of a Panel Data Proportional Hazards Model with Fixed Effects cemmap Working Papers CWP21/02, Institute for Fiscal Studies: London, UK, 2002. Also: http://www.cemmap.ac.uk/publications.php?publication_id=2649 Cohort(s): NLSY79 Publisher: Institute for Fiscal Studies (IFS), London Keyword(s): Data Analysis; Job Turnover; Modeling, Fixed Effects; Modeling, Hazard/Event History/Survival/Duration; Monte Carlo; Statistical Analysis; Work History Permission to reprint the abstract has not been received from the publisher. This paper considers a panel duration model that has a proportional hazards specification with fixed effects. The paper shows how to estimate the baseline and integrated baseline hazard functions without assuming that they belong to known, finite-dimensional families of functions. Existing estimators assume that the baseline hazard function belongs to a known parametric family. Therefore, the estimators presented here are more general than existing ones. This paper also presents a method for estimating the parametric part of the proportional hazards model under dependent right censoring, under which the partial likelihood estimator is inconsistent. The paper presents some Monte Carlo evidence on the small sample performance of the new estimators. Finally, the estimation methods are illustrated by applying them to National Longitudinal Survey of Youth work history data. The estimated, inverted U-shaped baseline hazard function of job ending suggests that the data are consistent with the job matching theory of Jovanovic (1979).
	Bibliography Citation Horowitz, Joel L. and Sokbae Lee. "Semiparametric Estimation of a Panel Data Proportional Hazards Model with Fixed Effects." cemmap Working Papers CWP21/02, Institute for Fiscal Studies: London, UK, 2002.

3.	Horowitz, Joel L. Lee, Sokbae
3.	Semiparametric Estimation of a Panel Data Proportional Hazards Model with Fixed Effects Journal of Econometrics 119,1 (March 2004): 155-198. Also: http://www.sciencedirect.com/science/article/pii/S0304407603002033 Cohort(s): NLSY79 Publisher: Elsevier Keyword(s): Modeling, Fixed Effects; Modeling, Hazard/Event History/Survival/Duration; Monte Carlo; Statistical Analysis; Work History This paper considers a panel duration model that has a proportional hazards specification with fixed effects. The paper shows how to estimate the baseline and integrated baseline hazard functions without assuming that they belong to known, finite-dimensional families of functions. Existing estimators assume that the baseline hazard function belongs to a known parametric family. Therefore, the estimators presented here are more general than existing ones. This paper also presents a method for estimating the parametric part of the proportional hazards model with dependent right censoring, under which the partial likelihood estimator is inconsistent. The paper presents some Monte Carlo evidence on the small sample performance of the new estimators.
	Bibliography Citation Horowitz, Joel L. and Sokbae Lee. "Semiparametric Estimation of a Panel Data Proportional Hazards Model with Fixed Effects." Journal of Econometrics 119,1 (March 2004): 155-198.

4.	Lee, Sokbae
4.	Essays on Semiparametric and Nonparametric Methods in Econometrics Ph.D. Dissertation, The University of Iowa, 2002. DAI-A 63/04, p. 1457, Oct 2002 Cohort(s): NLSY79 Publisher: UMI - University Microfilms, Bell and Howell Information and Learning Keyword(s): Human Capital; Modeling, Fixed Effects; Modeling, Hazard/Event History/Survival/Duration; Modeling, Mixed Effects; Modeling, Multilevel; Work History This dissertation consists of four chapters that deal with semiparametric and non-parametric problems in econometrics. The first chapter presents methods for estimating a conditional quantile function that is assumed to be partially linear. A simple, two-stage estimator of the parametric component of the conditional quantile is developed and the semiparametric efficiency bound for the parametric component is derived. Two types of efficient estimators are constructed. The estimation methods are applied to estimate the return to education in a human capital earnings function. Dimension reduction can be achieved in a different way. In the second chapter, the conditional quantile function is assumed to be additive. Individual additive components of the conditional quantile are estimated nonparametrically based on marginal integration. This chapter introduces a new pilot estimator and establishes the asymptotic distribution of the marginal integration estimator. The third chapter considers a panel duration model that has a proportional hazards specification with fixed effects. The chapter shows how to estimate the baseline and integrated baseline hazard functions without assuming that they belong to known, finite-dimensional families of functions. Existing estimators assume that the baseline hazard function belongs to a known parametric family. Therefore, the estimators presented here are more general than existing ones. This chapter also presents a method for estimating the parametric part of the proportional hazards model under dependent right censoring, under which the partial likelihood estimator is inconsistent. The estimation methods are illustrated by applying them to National Longitudinal Survey of Youth work history data. There are few a priori reasons for preferring one type of semiparametric model to other models. The final chapter reviews semiparametric methods for estimating conditional mean functions. The methods are illustrated by using them to estimate a model of the salaries of professional baseball players in the U.S. It is shown that the various semiparametric models can be distinguished empirically from each other and from a parametric model. The parametric model and several simple semiparametric models fail to capture important features of the data. However, a sufficiently rich semiparametric model describes the data well.
	Bibliography Citation Lee, Sokbae. Essays on Semiparametric and Nonparametric Methods in Econometrics. Ph.D. Dissertation, The University of Iowa, 2002. DAI-A 63/04, p. 1457, Oct 2002.

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