We propose a multiple-step procedure to compute average partial effects (APEs) for fixed-effects panel logit models estimated by Conditional Maximum Likelihood (CML). As individual effects are eliminated by conditioning on suitable sufficient statistics, we propose evaluating the APEs at the ML...

We propose a Markov chain Monte Carlo Conditional Maximum Likelihood (MCMC-CML) estimator for two-way fixed-effects logit models for dyadic data. The proposed MCMC approach, based on a Metropolis algorithm, allows us to overcome the computational issues of evaluating the probability of the...

We show that the h-index and the g-index, which are commonly used to mea- sure the research productivity of a scientist, may be seen as concentration indices. For these indices we also propose transformations that make them always ranging between two known limits, which correspond to the...

Evaluation of corrupt activities is incrementally based on administration of questionnaires to firms in business, and generally involves a large number of items. Data collected by questionnaires of this type can be analyzed by Latent Class (LC) models in order to classify firms into homogeneous...

We propose a test for state dependence in binary panel data under the dynamic logit model with individual covariates. For this aim, we rely on a quadratic exponential model in which the association between the response variables is accounted for in a different way with respect to more standard...

In the context of multilevel longitudinal data, where sample units are collected in clusters, an important aspect that should be accounted for is the unobserved heterogeneity between sample units and between clusters. For this aim we propose an approach based on nested hidden (latent) Markov...