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Measures of the explanatory power of general multivariate probit models
In this paper R 2 -type measures of the explanatory power of multivariate linear and categorical probit models proposed in the literature are reviewed and their deficiencies are discussed. It is argued that a measure of the explanatory power should take into account the components which are explicitely modeled when a regression model is estimated while it should be indifferent to components not explicitely modeled. Based on this view three different measures for multivariate probit models are proposed. Results of a simulation study are presented designed to compare two measures in various situations and evaluate the BC a bootstrap technique for testing the hypothesis that the corresponding measure is zero and to calculate approximate confidence intervals. The BC a bootstrap technique turned out to work quite well for a wide range of situations, but may lead to misleading results if the true values of the corresponding measure is close to zero. Key words: Pseudo- R 2 ; Measure of explanatory power; Multivariate probit model; Panel model; Simulation study; Bootstrap confidence intervals."
Spiess, M. & Tutz, G. (2002) ‘Measures of the explanatory power of general multivariate probit models’, EPAG Working Paper 2002-33. Colchester: University of Essex
M. Spiess, G. Tutz
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ECHP Waves
1994, 1995, 1996
Institutions Involved
Institute for Social and Economic Research, Essex
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