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The joint estimation of a non-linear labour supply function and a wage equation using simulated response probabilities

Author

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  • Bloemen, H.G.
  • Kapteyn, A.J.

    (Tilburg University, School of Economics and Management)

Abstract

When applying maximum likelihood estimation in jointly estimating a labour supply function and a wage equation, it may be practically impossible, both analytically and numerically, to calculate the required response probabilities, especially if the model is non-linear. As an alternative, we consider various simulation estimators. In both Monte Carlo experiments and empirical applications the methods are compared to each other and to ML. The methods are computationally feasible and perform well.
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Suggested Citation

  • Bloemen, H.G. & Kapteyn, A.J., 1992. "The joint estimation of a non-linear labour supply function and a wage equation using simulated response probabilities," Other publications TiSEM 82147653-e95c-457b-8bcc-5, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:82147653-e95c-457b-8bcc-51746658d7f6
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    2. Hans G. Bloemen & Arie Kapteyn, 2008. "The estimation of utility-consistent labor supply models by means of simulated scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(4), pages 395-422.
    3. Hajivassiliou, Vassilis A. & Ruud, Paul A., 1986. "Classical estimation methods for LDV models using simulation," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 40, pages 2383-2441, Elsevier.
    4. Vassilis A. Hajivassiliou, 1991. "Simulation Estimation Methods for Limited Dependent Variable Models," Cowles Foundation Discussion Papers 1007, Cowles Foundation for Research in Economics, Yale University.

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