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Title: Essays on Semiparametric and Nonparametric Methods in Econometrics
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Lee, Sokbae 
Essays on Semiparametric and Nonparametric Methods in Econometrics Ph.D. Dissertation, The University of Iowa, 2002. DAIA 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 nonparametric problems in econometrics. The first chapter presents methods for estimating a conditional quantile function that is assumed to be partially linear. A simple, twostage 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, finitedimensional 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. DAIA 63/04, p. 1457, Oct 2002. 