In this paper it is shown that the number of latent factors in a multiple multivariate regression model need not be larger than the number of the response variables in order to achieve an optimal prediction. The practical importance of this lemma is outlined and an application of such a...

When trying to interpret estimated parameters the researcher is interested in the (relative) importance of the individual predictors. However, if the predictors are highly correlated, the interpretation of coefficients, e.g. as economic ?multipliers?, is not applicable in standard regression or...

In simulation studies Latent Factor Prediction Pursuit outperformed classical reduced rank regression methods. The algorithm described so far for Latent Factor Prediction Pursuit had two shortcomings: It was only implemented for situations where the explanatory variables were of full colum rank....

Combined forecasts from a linear and a nonlinear model areinvestigated for timeseries with possibly nonlinear characteristics. The forecasts arecombined by aconstant coefficient regression method as well as a time varyingmethod. Thetime varying method allows for a locally (non)linear model....

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 t approximations [QERMit]. As a first step the...

Accurate prediction of risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) requires precise estimation of the tail of the predictive distribution. Two novel concepts are introduced that offer a specific focus on this part of the predictive density: the censored posterior, a...

This paper presents the Matlab package DeCo (Density Combination) which is based on the paper by Billio et al. (2013) where a constructive Bayesian approach is presented for combining predictive densities originating from different models or other sources of information. The combination weights...

A class of adaptive sampling methods is introduced for efficient posterior and predictive simulation. The proposed methods are robust in the sense that they can handle target distributions that exhibit non-elliptical shapes such as multimodality and skewness. The basic method makes use of...

We discuss Bayesian inferential procedures within the family of instrumental variables regression models and focus on two issues: existence conditions for posterior moments of the parameters of interest under a flat prior and the potential of Direct Monte Carlo (DMC) approaches for efficient...