In this paper we highlight a data augmentation approach to inference in the Bayesian logistic regression model. We demonstrate that the resulting conditional likelihood of the regression coefficients is multivariate normal, equivalent to a standard Bayesian linear regression, which allows for...

We propose a new class of state space models for longitudinal discrete response data where the observation equation is specified in an additive form involving both deterministic and random linear predictors. These models allow us to explicitly address the effects of trend, seasonal or other...

Regional employment volatility is an undesirable phenomenon which describes a strongly fluctuating pattern of employment, thus, "instability" of a local economy. In the literature on this field, much of the attention has been paid to two main issues. First, a group studies has investigated the...

We investigate the importance of taking the spatial interaction of turbines inside a wind park into account. This article provides two tests that check for wake effects and thus, take spatial interdependence into account. Those effects are suspected to have a negative influence on wind power...

We estimate a DSGE model for Brazil that includes both anticipated and unanticipated fiscal shocks. The model contains a relatively detailed public sector, which allows us to investigate the effects of anticipation for a much wider array of fiscal instruments than previously considered in the...

This paper builds a small size dynamic stochastic general equilibrium (DSGE) model with government, aiming to replicate key features of the Brazilian economy. I first calibrate and then I use Bayesian methods to estimate the model for Brazil, with 20 years of quarterly aggregate data. Contrary...