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Semiparametric three-step estimation methods for simultaneous equation systems

Author

Listed:
  • Stefan Sperlich

    (Departamento de Economía, Universidad Carlos III de Madrid, Spain)

  • Juan M. Rodríguez-Póo

    (Departamento de Análisis Económico, Universidad de Zaragoza, Spain)

  • Ana I. Fernández

    (Departamento de Econometría y Estadística, Universidad del País Vasco, Bilbao, Spain)

Abstract

This paper proposes a new method for estimating a structural model of labour supply in which hours of work depend on (log) wages and the wage rate is considered endogenous. The main innovation with respect to other related estimation procedures is that a nonparametric additive structure in the hours of work equation is permitted. Though the focus of the paper is on this particular application, a three-step methodology for estimating models in the presence of the above econometric problems is described. In the first step the reduced form parameters of the participation equation are estimated by a maximum likelihood procedure adapted for estimation of an additive nonparametric function. In the second step the structural parameters of the wage equation are estimated after obtaining the selection-corrected conditional mean function. Finally, in the third step the structural parameters of the labour supply equation are estimated using local maximum likelihood estimation techniques. The paper concludes with an application to illustrate the feasibility, performance and possible gain of using this method. Copyright © 2005 John Wiley & Sons, Ltd.

Suggested Citation

  • Stefan Sperlich & Juan M. Rodríguez-Póo & Ana I. Fernández, 2005. "Semiparametric three-step estimation methods for simultaneous equation systems," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(6), pages 699-721.
    • Handle: RePEc:jae:japmet:v:20:y:2005:i:6:p:699-721
    DOI: 10.1002/jae.796
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    References listed on IDEAS

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    1. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
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    3. Lee, Myoung-Jae, 1995. "Semi-parametric Estimation of Simultaneous Equations with Limited Dependent Variables: A Case Study of Female Labour Supply," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 187-200, April-Jun.
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    6. Fernández, Ana I. & Rodríguez-Póo, Juan M. & Sperlich, Stefan, 1998. "Semiparametric three step estimation methods in labor supply models," SFB 373 Discussion Papers 1998,71, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    7. Rand Ressler & Melissa Waters, 2000. "Female earnings and the divorce rate: a simultaneous equations model," Applied Economics, Taylor & Francis Journals, vol. 32(14), pages 1889-1898.
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    Cited by:

    1. Jing Dai & Stefan Sperlich & Walter Zucchini, 2011. "Estimating and Predicting Household Expenditures and Income Distributions," MAGKS Papers on Economics 201147, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    2. Patrick Saart & Jiti Gao & Nam Hyun Kim, 2014. "Semiparametric methods in nonlinear time series analysis: a selective review," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(1), pages 141-169, March.
    3. Moral-Arce, Ignacio & Rodríguez-Póo, Juan M. & Sperlich, Stefan, 2011. "Low dimensional semiparametric estimation in a censored regression model," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 118-129, January.

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