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Semiparametric estimation of a sampl...
~
Schafgans, Marcia Miranda Angelique.
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Semiparametric estimation of a sample selection model: Estimation of the intercept; theory and applications.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Semiparametric estimation of a sample selection model: Estimation of the intercept; theory and applications./
作者:
Schafgans, Marcia Miranda Angelique.
面頁冊數:
198 p.
附註:
Source: Dissertation Abstracts International, Volume: 57-06, Section: A, page: 2611.
Contained By:
Dissertation Abstracts International57-06A.
標題:
Economics, Theory. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9635382
Semiparametric estimation of a sample selection model: Estimation of the intercept; theory and applications.
Schafgans, Marcia Miranda Angelique.
Semiparametric estimation of a sample selection model: Estimation of the intercept; theory and applications.
- 198 p.
Source: Dissertation Abstracts International, Volume: 57-06, Section: A, page: 2611.
Thesis (Ph.D.)--Yale University, 1996.
Standard approaches to the estimation of sample selection models impose that the errors have a bivariate normal distribution. It is known that these estimators are inconsistent under non-normality. As a result, important progress on semiparametric estimation of sample selection models has been made in the last decade.Subjects--Topical Terms:
1017575
Economics, Theory.
Semiparametric estimation of a sample selection model: Estimation of the intercept; theory and applications.
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Source: Dissertation Abstracts International, Volume: 57-06, Section: A, page: 2611.
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Standard approaches to the estimation of sample selection models impose that the errors have a bivariate normal distribution. It is known that these estimators are inconsistent under non-normality. As a result, important progress on semiparametric estimation of sample selection models has been made in the last decade.
520
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In the semiparametric literature, estimation of the intercept typically has been subsumed in the nonparametric sample selectivity bias correction term. The estimation of this parameter, however, has important economic relevance. For instance, it allows one to determine the wage gap between unionized and nonunionized workers, decompose the wage differential between different socio-economic groups (e.g., male-female), and evaluate the net benefits of a social program.
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The first chapter, therefore, gives a consistent asymptotically normal estimator for the intercept of a semiparametrically estimated sample selection model. To achieve this, a decreasingly small fraction of all observations are used for the estimation of this intercept.
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In the second chapter, an expression is derived for the inconsistency of the two-step Heckman (1976, 1979) estimator under non-normality. In particular, the inconsistency of the intercept of the outcome equation of this estimator is considered. This estimator is then compared with the consistent semiparametric alternative developed in Chapter 1. Using a root mean squared error criterion, the semiparametric estimator performs better for a range of bandwidth parameter choices for a variety of distributions of the errors and regressors. For error distributions that are close to the normal, however, the two-step parametric estimator performs better.
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The last chapter is an empirical study on the labor force in (Peninsular) Malaysia. Both parametric and semiparametric estimation techniques are used for the estimation of wage functions. Special focus is given on the effect the semiparametric estimation techniques have on the returns to education. The equations are then used to assess the extent of ethnic and gender 'discrimination'--i.e., the part of the wage-gap that cannot be attributed to differences in wage determining characteristics. The newly developed estimator is applied to establish a consistent estimator of the intercept of the wage functions in the semiparametrically estimated sample selection model.
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School code: 0265.
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