This paper provides methods for carrying out likelihood based inference for diffusion driven models, for example discretely observed multivariate diffusions, continuous time stochastic volatility models and counting process models. The diffusions can potentially be non-stationary. Although our...

This article is concerned with the analysis of correlated count data. A class of models is proposed in which the correlation among the counts is represented by correlated latent effects. Special cases of the model are discussed and a tuned and efficient Markov chain Monte Carlo algorithm is...

This paper is concerned with the problems of posterior simulation and model choice for Poisson panel data models with multiple random effects. Efficient algorithms based on Markov Chain Monte Carlo methods for sampling the posterior distribution are developed. A new parameterization of the...

This paper provides a unified simulation-based Bayesian and non-Bayesian analysis of correlated binary data using the multivariate probit model. The posterior distribution is simulated by Markov chain Monte Carlo methods, and maximum likelihood estimates are obtained by a Markov chain Monte...

In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective method is developed that samples all the unobserved volatilities at once using an approximating...

Lubik and Schorfheide (2004) extend estimated DSGE models to address monetary policy indeterminacy. Their method leads to an all-or-none classification of a time period as having determinate or indeterminate monetary policy. Sub-sample estimates indicate, however, that U.S. monetary policy might...

This talk discusses and illustrates a software environment for conducting Bayesian calculations for a variety of models including those for discrete and panel data. The software is self-contained and provides spreadsheet/ graphics facilities for manipulating both input and output data from the...

This article presents a non-Markovian regime switching model in which the regime states depend on the sign of an autoregressive latent variable. The magnitude of the latent variable indexes the 'strength' of the state or how deeply the system is embedded in the current regime. In this model,...

Given the advent of basket-level purchasing data of households, choice modelers are actively engaged in the development of statistical and econometric models of multi-category choice behavior of households. This paper reviews current developments in this area of research, discussing the modeling...