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AMH Copula ML Estimation for the Sample Selection Model

Title
AMH Copula ML Estimation for the Sample Selection Model
Authors
Song, Hosin
Ewha Authors
송호신
SCOPUS Author ID
송호신scopus
Issue Date
2016
Journal Title
KOREAN ECONOMIC REVIEW
ISSN
0254-3737JCR Link
Citation
vol. 32, no. 2, pp. 239 - 268
Keywords
sample selectionAli-Mikhail-Haq copula MLbivariate MLHeckman's two-step estimation
Publisher
KOREAN ECONOMIC ASSOCIATION
Indexed
SSCI; SCOPUS; KCI WOS scopus
Abstract
In this paper, we propose a copula ML estimation method for the sample selection model using the Ali-Mikhail-Haq (AMH) copula. The proposed AMH copula ML estimation is compared with the well-known bivariate ML estimation and Heckman's two-step estimation. Monte Carlo experiments are conducted to compare their performance in terms of the mean squared error (MSE) depending on the following 2 conditions: (i) whether the imposed distributional assumption is correct, and (ii) whether some regressors of the participation and outcome equation are correlated. The results of the experiments show that the estimation results for the proposed method can be better than those of the two well-known methods, particularly when the imposed distributional assumption is incorrect and some regressors of the two equations are correlated. Hence, the proposed method can be a practically useful alternative for the sample selection model.
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사회과학대학 > 경제학전공 > Journal papers
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