We propose a simulated maximum likelihood estimator for dynamic models based on non-parametric kernel methods. Our … method is designed for models without latent dynamics from which one can simulate observations but cannot obtain a closed … dynamic models that this nonparametric simulated maximum likelihood (NPSML) estimator is consistent and asymptotically …

In this paper R2-type measures of the explanatory power of multivariate linear and categorical probit models proposed … 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 …

In this paper we discuss parameter identification and likelihood evaluation for multinomial multiperiod Probit models …

. We find that there are no relevant differences in point estimates given by the two models, even for small sample size …

proposed class of regression models is not only less complex but can account for more versatile dependence structures and still … allow for an intuitive explanation. We examine some of the properties possessed by this class of regression models and show … its connections to several other models. For fitting purposes, we use the expectation-maximization (EM) algorithm which we …

In this paper we discuss parameter identification and likelihood evaluation for multinomial multiperiod Probit models …

heading of item response models (IRM). In Econometrics, subjective well-being (SWB) and self-assessed health (SAH) studies …, and in marketing research, Ordered Probit, Ordered Logit, and Interval Regression models are common research platforms. To … single-equation models estimation of the latent linear model by Maximum Likelihood (ML) is routine. But, for higher …