No 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 consistentiy well under our criterion. We also very briefly discuss Pseudo-R2 measures for count data, for duration models and for prediction-realization tables.

No abstract

No abstract

Michael Veall is a QSEP Research Associate and a member of the McMaster Department of Economics. Mary-Anne Sillamaa is with Statistics Canada. This report is cross-listed as No. 25 in the McMaster University SEDAP Research Paper Series. The Research Institute for Quantitative Studies in Economics and Population (QSEP) is an interdisciplinary institute established at McMaster University to encourage and facilitate theoretical and empirical studies in economics, population, and related fields. For further information about QSEP and other reports in this series, see our web site http://socserv2.mcmaster.ca/~qsep. The Research Report series provides a vehicle for distributing the results of studies undertaken by QSEP associates. Authors take full responsibility for all expressions of opinion.