Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/117953
Authors: 
Strazzera, Elisabetta
Genius, Margarita
Year of Publication: 
2004
Series/Report no.: 
Nota di Lavoro, Fondazione Eni Enrico Mattei 73.2004
Abstract: 
The sample selection model is based upon a bivariate or a multivariate structure, and distributional assumptions are in this context more severe than in univariate settings, due to the limited availability of tractable multivariate distributions. While the standard FIML estimation of the selectivity model assumes normality of the joint distribution, alternative approaches require less stringent distributional hypotheses. As shown by Smith (2003), copulas allow great flexibility also in FIML models. The copula model is very useful in situations where the applied researcher has a prior on the distributional form of the margins, since it allows separating their modelling from that of the dependence structure. In the present paper the copula approach to sample selection is first compared to the semiparametric approach and to the standard FIML, bivariate normal model, in an illustrative application on female work data. Then its performance is analysed more thoroughly in an application to Contingent Valuation data on recreational values of forests.
Subjects: 
Contingent valuation
Selectivity bias
Bivariate models
Copulas
JEL: 
C34
C51
H41
Q26
Document Type: 
Working Paper

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