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Heckman method and switching regression model multivariate generalization

Author

Listed:
  • Kossova, Elena

    (National Research University Higher School of Economics (NRU HSE), Moscow, Russian Federation)

  • Potanin, Bogdan

    (National Research University Higher School of Economics (NRU HSE), Moscow, Russian Federation)

Abstract

The article is devoted to simultaneous estimation of one continuous and various binary equations under assumption of disturbances joint normality. It generalizes Heckman selection and switch-probit models to multivariate case. Following Heckman’s univariate model implementation both two step and maximum likelihood procedures are provided. In order to test model performance and correctness we execute analysis on simulated data. It shows that when there are two selection equations generalized model estimates accuracy noticeably outperforms those that are obtained using least squares or Heckman’s methods.

Suggested Citation

  • Kossova, Elena & Potanin, Bogdan, 2018. "Heckman method and switching regression model multivariate generalization," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 50, pages 114-143.
    • Handle: RePEc:ris:apltrx:0346
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    References listed on IDEAS

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    Cited by:

    1. B. S. Potanin, 2019. "Estimating the Effect of Higher Education on an Employee’s Wage," Studies on Russian Economic Development, Springer, vol. 30(3), pages 319-326, May.
    2. Kossova, Elena & Kupriianova, Liubov & Potanin, Bogdan, 2020. "Parametric and semiparametric multivariate sample selection models estimators’ accuracy: Comparative analysis on simulated data," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 57, pages 119-139.

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    More about this item

    Keywords

    sample selection; switching regression model;

    JEL classification:

    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models

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