Important choices for efficient and accurate evaluation of marginal likelihoods by means of Monte Carlo simulation methods are studied for the case of highly non-elliptical posterior distributions. We focus on the situation where one makes use of importance sampling or the independence chain...

An efficient and accurate approach is proposed for forecasting Value at Risk [VaR] and Expected Shortfall [ES] measures in a Bayesian framework. This consists of a new adaptive importance sampling method for Quantile Estimation via Rapid Mixture of <I>t</I> approximations [QERMit]. As a first step the...</i>

We propose new forecast combination schemes for predicting turning points of business cycles. The combination schemes deal with the forecasting performance of a given set of models and possibly providing better turning point predictions. We consider turning point predictions generated by...

We construct models which enable a decision-maker to analyze the implications of typical time series patterns of daily exchange rates for currency risk management. Our approach is Bayesian where extensive use is made of Markov chain Monte Carlo methods. The effects of several model...

Exchange rates typically exhibit time-varying patterns in both means and variances. The histograms of such series indicate heavy tails. In this paper we construct models which enable a decision-maker to analyze the implications of such time series patterns for currency risk management. Our...

Adaptive Polar Sampling (APS) is proposed as a Markov chain Monte Carlo method for Bayesian analysis of models with ill-behaved posterior distributions. In order to sample efficiently from such a distribution, a location-scale transformation and a transformation to polar coordinates are used....

The failure to describe the time series behaviour of most real exchange rates as temporary deviations from fixed long-term means may be due to time variation of the equilibria themselves, see Engel (2000). We implement this idea using an unobserved components model and decompose the observations...

This paper presents the R package AdMit which provides functions to approximate and sample from a certain target distribution given only a kernel of the target density function. The core algorithm consists in the function AdMit which fits an adaptive mixture of Student-t distributions to the...

Several lessons learned from a Bayesian analysis of basic economic time series models by means of the Gibbs sampling algorithm are presented. Models include the Cochrane-Orcutt model for serial correlation, the Koyck distributed lag model, the Unit Root model, the Instrumental Variables model...

Highly non-elliptical posterior distributions may occur in several econometric models, in particular, when the likelihood information is allowed to dominate and data information is weak. We explain the issue of highly non-elliptical posteriors in a model for the effect of education on income...