The paper compares two approaches to the estimation of panel probit models: the Generalized Method of Moments (GMM) and the Simulated Maximum Likelihood (SML) technique. Both have in common that they circumvent multiple integrations of joint density functions without the need to impose...

This paper compares generalized method of moments (GMM) and simulated maximum likeli- hood (SML) approaches to the estimation of the panel probit model. Both techniques circumvent multiple integration of joint density functions without the need to restrict the error term variance-covariance...

The paper compares two approaches to the estimation of panel probit models: the Generalized Method of Moments (GMM) and the Simulated Maximum Likelihood (SML) technique. Both have in common that they circumvent multiple integrations of joint density functions without the need to impose...

Consideration of latent heterogeneity is of special importance in non linear models for gauging correctly the effect of explaining variables on the dependent variable. This paper adopts the stratified model-based clustering approach for modeling latent heterogeneity for panel probit models....

A simulation study designed to evaluate the pseudo-R2 proposed by Spiess and Keller (1999) suggests that this measure represents the goodness-of-fit not only of the systematic part, but also of the assumed correlation structure in binary panel probit models.

This paper develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. The new approach can easily deal with the commonly encountered and widely discussed initial conditions problem, as well as the...

This paper develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. The new approach can easily deal with the commonly encountered and widely discussed "initial conditions problem," as well as the...

We develop a panel data count model combined with a latent Gaussian spatio-temporal heterogenous state process to analyze monthly severe crimes at the census tract level in Pittsburgh, Pennsylvania. Our data set combines Uniform Crime Reporting data with socio-economic data from the 2000 census....

M-PRESS-CreditRisk is a new top-down macro stress testing framework that can help supervisors gauge banks' capital adequacy related to credit risk. For the first time, it combines calibration of microprudential capital requirements and macroprudential buffers in a unified, coherent framework....