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Essays in Econometrics
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Essays in Econometrics. |
| 개인저자 | Laage, Louise. |
| 단체저자명 | Yale University. Economics. |
| 발행사항 | [S.l.]: Yale University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 195 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04A. Dissertation Abstract International |
| ISBN | 9781088318713 |
| 학위논문주기 | Thesis (Ph.D.)--Yale University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: A.
Advisor: Kitamura, Yuichi. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Chapter 1: This paper studies a class of linear panel models with random coefficients. We do not restrict the joint distribution of the time-invariant unobserved heterogeneity and the covariates. We investigate identification of the average partial effect (APE) when fixed-effect techniques cannot be used to control for the correlation between the regressors and the time-varying disturbances. Relying on control variables, we develop a constructive two-step identification argument. The first step identifies nonparametrically the conditional expectation of the disturbances given the regressors and the control variables, and the second step uses "between-group" variations, correcting for endogeneity, to identify the APE. We propose a natural semiparametric estimator of the APE, show its root n asymptotic normality and compute its asymptotic variance. The estimator is computationally easy to implement, and Monte Carlo simulations show favorable finite sample properties. Control variables arise in various economic and econometric models, and we provide variations of our argument to obtain identification in some applications. As an empirical illustration, we estimate the average elasticity of intertemporal substitution in a labor supply model with random coefficients.Chapter 2, joint with Y. Kitamura: Finite mixture models are useful in applied econometrics. They can be used to model unobserved heterogeneity, which plays major roles in labor economics, industrial organization and other fields. Mixtures are also convenient in dealing with contaminated sampling models and models with multiple equilibria. This paper shows that finite mixture models are nonparametrically identified under weak assumptions that are plausible in economic applications. The key is to utilize the identification power implied by information in covariates variation. First, three identification approaches are presented, under distinct and non-nested sets of sufficient conditions. Observable features of data inform us which of the three approaches is valid. These results apply to general nonparametric switching regressions, as well as to structural econometric models, such as auction models with unobserved heterogeneity. Second, some extensions of the identification results are developed. In particular, a mixture regression where the mixing weights depend on the value of the regressors in a fully unrestricted manner is shown to be nonparametrically identifiable. This means a finite mixture model with function-valued unobserved heterogeneity can be identified in a cross-section setting, without restricting the dependence pattern between the regressor and the unobserved heterogeneity. In this aspect it is akin to fixed effects panel data models which permit unrestricted correlation between unobserved heterogeneity and covariates. Third, the paper shows that fully nonparametric estimation of the entire mixture model is possible, by forming a sample analogue of one of the new identification strategies. The estimator is shown to possess a desirable polynomial rate of convergence as in a standard nonparametric estimation problem, despite nonregular features of the model. |
| 일반주제명 | Economics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
