PSEUDO‐ R 2 MEASURES FOR SOME COMMON LIMITED DEPENDENT VARIABLE MODELS

PSEUDO‐ R 2 MEASURES FOR SOME COMMON LIMITED DEPENDENT VARIABLE MODELS Abstract. A large number of different Pseudo‐R2 measures for some common limited dependent variable models are surveyed. Measures include those based solely on the maximized likelihoods with and without the restriction that slope coefficients are zero, those which require further calculations based on parameter estimates of the coefficients and variances and those that are based solely on whether the qualitative predictions of the model are correct or not. The theme of the survey is that while there is no obvious criterion for choosing which Pseudo‐R2 to use, if the estimation is in the context of an underlying latent dependent variable model, a case can be made for basing the choice on the strength of the numerical relationship to the OLS‐R2 in the latent dependent variable. As such an OLS‐R2 can be known in a Monte Carlo simulation, we summarize Monte Carlo results for some important latent dependent variable models (binary probit, ordinal probit and Tobit) and find that a Pseudo‐R2 measure due to McKelvey and Zavoina scores consistently well under our criterion. We also very briefly discuss Pseudo‐R2 measures for count data, for duration models and for prediction‐realization tables. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Economic Surveys Wiley

PSEUDO‐ R 2 MEASURES FOR SOME COMMON LIMITED DEPENDENT VARIABLE MODELS

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Publisher
Wiley
Copyright
Copyright © 1996 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0950-0804
eISSN
1467-6419
DOI
10.1111/j.1467-6419.1996.tb00013.x
Publisher site
See Article on Publisher Site

Abstract

Abstract. A large number of different Pseudo‐R2 measures for some common limited dependent variable models are surveyed. Measures include those based solely on the maximized likelihoods with and without the restriction that slope coefficients are zero, those which require further calculations based on parameter estimates of the coefficients and variances and those that are based solely on whether the qualitative predictions of the model are correct or not. The theme of the survey is that while there is no obvious criterion for choosing which Pseudo‐R2 to use, if the estimation is in the context of an underlying latent dependent variable model, a case can be made for basing the choice on the strength of the numerical relationship to the OLS‐R2 in the latent dependent variable. As such an OLS‐R2 can be known in a Monte Carlo simulation, we summarize Monte Carlo results for some important latent dependent variable models (binary probit, ordinal probit and Tobit) and find that a Pseudo‐R2 measure due to McKelvey and Zavoina scores consistently well under our criterion. We also very briefly discuss Pseudo‐R2 measures for count data, for duration models and for prediction‐realization tables.

Journal

Journal of Economic SurveysWiley

Published: Sep 1, 1996

References

  • Diagnostic Tests for Models Based on Individual Data: A Survey
    Pagan, Pagan; Vella, Vella
  • Goodness of fit measures in the Tobit Model
    Veall, Veall; Zimmermann, Zimmermann
  • Recent Developments in Count Data Modelling: Theory and Applications
    Winkelmann, Winkelmann; Zimmermann, Zimmermann

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