EDGEWORTH EXPANSION BASED CORRECTION OF SELECTIVITY BIAS IN MODELS OF DOUBLE SELECTION

Insan Tunali () and Berk Yavuzoglu
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Insan Tunali: Department of Economics, Koc University

No 1802, Working Papers from Nazarbayev University, Department of Economics

Abstract: Edgeworth expansions are known to be useful for approximating probability distributions and moments. In our case, we exploit the expansion in the context of models of double selection embedded in a trivariate normal structure. We assume bivariate normality among the random disturbance terms in the two selection equations but allow the distribution of the disturbance term in the outcome equation to be free. This sets the stage for a control function approach to correction of selectivity bias that affords tests for the more common trivariate normality specifi- cation. Other recently proposed methods for handling multiple outcomes are Multinomial Logit based selection correction models. An empirical example is presented to document the dierences among the results obtained from our selectivity correction approach, trivariate normality specification and Multinomial Logit based selection correction models.

Keywords: double selection models; Edgeworth expansion; female labor supply; Multinomial Logit based selection correction models; selectivity bias. (search for similar items in EconPapers)
Pages: 51 pages
Date: 2018-11, Revised 2018-11
New Economics Papers: this item is included in nep-ecm
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