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A Generalized Two-Part Model for Fractional Response Variables with Excess Zeros

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  • Schwiebert, Jörg
  • Wagner, Joachim

Abstract

The fractional probit (or fractional logit) model is used when the outcome variable is a fractional response variable, i.e. a variable taking a value between zero and one. In case of excess zeros, the fractional probit model might not be the optimal modeling device since this model does not predict zeros. As a solution, the two-part model has been proposed, which assumes different processes for having a (non-)zero outcome and, conditionally on having a non-zero outcome, the actual outcome. However, the two-part model assumes independence of these processes. This paper proposes a generalization of the two-part model which allows for dependence of these processes and which also nests the two-part model as a special case. A simulation study indicates that the proposed estimator performs well in finite samples. Two empirical examples illustrate that the model proposed in this paper improves upon the fractional probit and two-part model in terms of model fit and also leads to different marginal effects.

Suggested Citation

  • Schwiebert, Jörg & Wagner, Joachim, 2015. "A Generalized Two-Part Model for Fractional Response Variables with Excess Zeros," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113059, Verein für Socialpolitik / German Economic Association.
    • Handle: RePEc:zbw:vfsc15:113059
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    References listed on IDEAS

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    3. Esmeralda A. Ramalho & Joaquim J.S. Ramalho & José M.R. Murteira, 2011. "Alternative Estimating And Testing Empirical Strategies For Fractional Regression Models," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 19-68, February.
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    Cited by:

    1. Schwiebert, Jörg, 2016. "A Sample Selection Model for Fractional Response Variables," VfS Annual Conference 2016 (Augsburg): Demographic Change 145527, Verein für Socialpolitik / German Economic Association.

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    More about this item

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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