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The copula approach of sampling selection modelling: an application to the recreational value of forests

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.

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Paper provided by Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia in its series Working Paper CRENoS with number 200308.

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Date of creation: 2003
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Handle: RePEc:cns:cnscwp:200308
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  1. Leung, S.F. & Yu, S., 1996. "Collinearity and Two-Step Estimation of Sample Selection Models: Problems, Origins and Remedies," RCER Working Papers 419, University of Rochester - Center for Economic Research (RCER).
  2. Horowitz, Joel L., 1993. "Semiparametric estimation of a work-trip mode choice model," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 49-70, July.
  3. Lee, Lung-Fei, 1983. "Generalized Econometric Models with Selectivity," Econometrica, Econometric Society, vol. 51(2), pages 507-12, March.
  4. Heckman, J J & Tobias, Justin & Vytlacil, Ed, 2003. "Simple Estimators for Treatment Parameters in a Latent Variable Framework," Staff General Research Papers 12012, Iowa State University, Department of Economics.
  5. Lee, Lung-Fei, 1982. "Some Approaches to the Correction of Selectivity Bias," Review of Economic Studies, Wiley Blackwell, vol. 49(3), pages 355-72, July.
  6. Leung, S.F. & Yu, S., 1992. "On the Choice Between Sample Selection and Two-Part Models," RCER Working Papers 337, University of Rochester - Center for Economic Research (RCER).
  7. Puhani, Patrick A, 2000. " The Heckman Correction for Sample Selection and Its Critique," Journal of Economic Surveys, Wiley Blackwell, vol. 14(1), pages 53-68, February.
  8. Heckman, James J, 1974. "Shadow Prices, Market Wages, and Labor Supply," Econometrica, Econometric Society, vol. 42(4), pages 679-94, July.
  9. James J. Heckman & Justin L. Tobias & Edward Vytlacil, 2000. "Simple Estimators for Treatment Parameters in a Latent Variable Framework with an Application to Estimating the Returns to Schooling," NBER Working Papers 7950, National Bureau of Economic Research, Inc.
  10. Olsen, Randall J, 1980. "A Least Squares Correction for Selectivity Bias," Econometrica, Econometric Society, vol. 48(7), pages 1815-20, November.
  11. Pagan, Adrian & Vella, Frank, 1989. "Diagnostic Tests for Models Based on Individual Data: A Survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 4(S), pages S29-59, Supplemen.
  12. Maria Fraga O. Martins, 2001. "Parametric and semiparametric estimation of sample selection models: an empirical application to the female labour force in Portugal," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(1), pages 23-39.
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