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

Author

Listed:
  • Kossova, Elena

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

  • Kupriianova, Liubov

    (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

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

  • 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.
    • Handle: RePEc:ris:apltrx:0391
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

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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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