GARCH models are commonly used as latent processes in econometrics, financial economics, and macroeconomics. Yet no exact likelihood analysis of these models has been provided so far. In this paper we outline the issues and suggest a Markov chain Monte Carlo algorithm which allows the...

This paper is concerned with the Bayesian estimation and comparison of flexible, high dimensional multivariate time series models with time varying correlations. The model proposed and considered here combines features of the classical factor model with that of the heavy tailed univariate...

In 2003, an industry-financed, government-administered buyback of trawl fishing permits and vessels took place on the US West Coast, resulting in the retirement of about one-third of the limited-entry trawl fleet. The lack of cost data in this fishery precludes an analysis of how the buyback has...

The present dissertation consists of three stand-alone research papers that all deal with factor models from a Bayesian perspective, both in a theoretical and an empirical setup. More precisely, the thesis is organized in a progressive way as follows: Chapter 1 briefly presents the general...

We present an hierarchical Bayes approach to modeling parameter heterogeneity in generalized linear models. The model assumes that there are relevant subpopulations and that within each subpopulation the individual-level regression coefficients have a multivariate normal distribution. However,...

The point source is the most common type of source to be modeled for its effect on air pollution. Point sources provide auxiliary information that may impact both the mean and covariance structure of measured responses, but these possible impacts are often overlooked by spatial modelers. In this...

The article examines the properties of generalized method of moments GMM estimators of utility function parameters. The research strategy is to apply the GMM procedure to generated data on asset returns from stochastic exchange economies; discrete methods and Markov chain models are used to...

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient...

In this paper we assess the short-term forecasting power of different time series models in the electricity spot market. In particular we calibrate AR/ARX ("X" stands for exogenous/fundamental variable—system load in our study), AR/ARX-GARCH, TAR/TARX and Markov regime-switching models to...