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Parametric and semiparametric multivariate sample selection models estimators’ accuracy: Comparative analysis on simulated data

Author

Listed:
  • Elena Kossova

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

  • Liubov Kupriianova

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

  • Bogdan Potanin

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

Abstract

This article is devoted to the comparative analysis of parametric and semiparametric sample selection models with two selection equations. Comparison has been conducted on simulated data under different random errors distributional assumptions: student, beta and mixture of normal. The results suggest that for student and beta distributions parametric models’ estimates are more or equally accurate as semiparametric. However, former methods provide more accurate estimates under mixture distribution case. Therefore, parametric sample selection model estimators seem to be robust to violations of normality assumption in terms of tails thickness and asymmetry but fail to account for bimodality as good as their semiparametric counterparts

Suggested Citation

  • Elena Kossova & Liubov Kupriianova & Bogdan Potanin, 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.
    • Handle: RePEc:ris:apltrx:0391
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    References listed on IDEAS

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    1. Giuseppe De Luca & Franco Peracchi, 2012. "Estimating Engel curves under unit and item nonresponse," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(7), pages 1076-1099, November.
    2. Racine, Jeffrey S., 2008. "Nonparametric Econometrics: A Primer," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(1), pages 1-88, March.
    3. 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.
    4. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    5. Joe, Harry, 2006. "Generating random correlation matrices based on partial correlations," Journal of Multivariate Analysis, Elsevier, vol. 97(10), pages 2177-2189, November.
    6. Whitney K. Newey, 2009. "Two-step series estimation of sample selection models," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages 217-229, January.
    7. Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
    8. Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-390, March.
    9. Mitali Das & Whitney K. Newey & Francis Vella, 2003. "Nonparametric Estimation of Sample Selection Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 70(1), pages 33-58.
    10. Pigini Claudia, 2015. "Bivariate Non-Normality in the Sample Selection Model," Journal of Econometric Methods, De Gruyter, vol. 4(1), pages 123-144, January.
    11. Elena Kossova & Bogdan Potanin, 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.
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    Cited by:

    1. Elena Kossova & Bogdan Potanin, 2022. "Estimation of Gaussian multinomial endogenous switching model," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 67, pages 121-143.
    2. Elena Kossova & Mariia Kosorukova, 2023. "Estimation of the treatment effect of higher education on health: Comparison of the multivariate recursive probit model and matching," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 69, pages 65-90.

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    More about this item

    Keywords

    sample selection; heavy-tailed asymmetric bimodal random error distributions; semi-parametric models;
    All these keywords.

    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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