Comparison of performance measures for multivariate discrete models

Discrete copula model, Multivariate probit model, Goodness of fit measures, Simulation,

Pseudo-R"2 measures for some common limited dependent variable models

A large number of different Pseudo-R"2 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 zeor, 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-R"2 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-R"2 in the latent dependent variable. As such an OLS-R"2 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-R"2 measure due to McKelvey and Zavonia scores consistently well under our criterion. We also very briefly discuss Pseudo-R"2 measures for count data, for duration models and for prediction-realization tables. (orig.)Available from TIB Hannover: RR 6137(18) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

Pseudo-R"2 measures for some common limited dependent variable models

A large number of different Pseudo-R"2 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 zeor, 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-R"2 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-R"2 in the latent dependent variable. As such an OLS-R"2 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-R"2 measure due to McKelvey and Zavonia scores consistently well under our criterion. We also very briefly discuss Pseudo-R"2 measures for count data, for duration models and for prediction-realization tables. (orig.)Available from TIB Hannover: RR 6137(18) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

The Size and Power of the Bias-Corrected Bootstrap Test for Regression Models with Autocorrelated Errors

This paper is concerned with statistical inference for the coefficient of the linear regression model when the error term follows an autoregressive (AR) model. Past studies have reported severe size distortions, when the data are trending and autocorrelation of the error term is high. In this paper, we consider a test based on the bias-corrected bootstrap, where bias-corrected parameter estimators for the AR and regression coefficients are used. For bias-correction, the jackknife and bootstrap methods are employed. Monte Carlo simulations are conducted to compare size and power properties of the bias-corrected bootstrap test. It is found that the bias-corrected bootstrap test shows substantially improved size properties and exhibits excellent power for most of cases considered. It also appears that bootstrap bias-correction leads to better size and higher power values than jackknife bias-correction. These results are found to be robust to the choice of parameter estimation methods. Copyright Springer Science + Business Media, Inc. 2005bias-correction, bootstrap, jackknife, statistical inference,