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A Two-Stage Estimator for Probit Models with Structural Group Effects

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  • George J. Borjas
  • Glenn T. Sueyoshi

Abstract

This paper outlines a two-stage technique for estimation and inference in probit models with structural group effects. The structural group specification belongs to a broader class of random components models. In particular, individuals in a given group share a common component in the specification of the conditional mean of a latent variable. For a number of computational reasons, existing random-effects models are impractical for estimation and inference in this type of problem. Our two-stage estimator provides an easily estimable alternative to the random effect specification. In addition, we conduct a Monte Carlo simulation comparing the performance of alternative estimators, and find that the two-stage estimator is superior -- both in terms of estimation and inference -- to traditional estimators.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0146.

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Date of creation: Nov 1993
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Publication status: published as Borjas, George J. and Glenn T. Sueyoshi. "A Two-Stage Estimator For Probit Models With Structural Group Effects," Journal of Econometrics, 1994, v64(1/2), 165-182.
Handle: RePEc:nbr:nberte:0146

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  1. Avery, Robert B & Hansen, Lars Peter & Hotz, V Joseph, 1983. "Multiperiod Probit Models and Orthogonality Condition Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 24(1), pages 21-35, February.
  2. Borjas, George J, 1987. "Self-Selection and the Earnings of Immigrants," American Economic Review, American Economic Association, vol. 77(4), pages 531-53, September.
  3. Hausman, Jerry A & Taylor, William E, 1981. "Panel Data and Unobservable Individual Effects," Econometrica, Econometric Society, vol. 49(6), pages 1377-98, November.
  4. Borjas, George J, 1992. "Ethnic Capital and Intergenerational Mobility," The Quarterly Journal of Economics, MIT Press, vol. 107(1), pages 123-50, February.
  5. Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318 Elsevier.
  6. Robinson, Peter M, 1982. "On the Asymptotic Properties of Estimators of Models Containing Limited Dependent Variables," Econometrica, Econometric Society, vol. 50(1), pages 27-41, January.
  7. Chamberlain, Gary, 1980. "Analysis of Covariance with Qualitative Data," Review of Economic Studies, Wiley Blackwell, vol. 47(1), pages 225-38, January.
  8. Hsiao,Cheng, 2003. "Analysis of Panel Data," Cambridge Books, Cambridge University Press, number 9780521818551, October.
  9. James J. Heckman & Robert J. Willis, 1974. "Estimation of a Stochastic Model of Reproduction: An Econometric Approach," NBER Working Papers 0034, National Bureau of Economic Research, Inc.
  10. Moulton, Brent R., 1986. "Random group effects and the precision of regression estimates," Journal of Econometrics, Elsevier, vol. 32(3), pages 385-397, August.
  11. Blanchflower, D. & Oswald, A., 1989. "The Wage Curve," Papers 340, London School of Economics - Centre for Labour Economics.
  12. Amemiya, Takeshi, 1978. "A Note on a Random Coefficients Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 19(3), pages 793-96, October.
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