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The Joint Estimation of a Non-Linear Labour Supply Function and a Wage Equation Using Simulated Response Probabilities

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  • Hans G. Bloemen
  • Arie Kapteyn

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.

Suggested Citation

  • Hans G. Bloemen & Arie Kapteyn, 1993. "The Joint Estimation of a Non-Linear Labour Supply Function and a Wage Equation Using Simulated Response Probabilities," Annals of Economics and Statistics, GENES, issue 29, pages 175-205.
    • Handle: RePEc:adr:anecst:y:1993:i:29:p:175-205
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    Cited by:

    1. 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.
    2. 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.
    3. 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.

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