In this paper we consider regression models for count data allowing for overdispersion in a Bayesian framework. We account for unobserved heterogeneity in the data in two ways. On the one hand, we consider more flexible models than a common Poisson model allowing for overdispersion in different...

In this paper we consider regression models for count data allowing for overdispersion in a Bayesian framework. Besides the inclusion of covariates, spatial effects are incorporated and modelled using a proper Gaussian conditional autoregressive prior based on Pettitt et al. (2002). Apart from...

Does the ranking of Covid-19 cases by municipality follow Zipf’s law (i.e. an estimated Pareto exponent of one)? This note tries to answer this question using daily data from Brazil for the Mar 30, 2020 -Oct 22, 2021 period. We used a Poisson Pseudo Maximum Likelihood (PPML) estimator and the...