A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or linear pools, evaluated using the conventional log predictive scoring rule. The log score is a concave...

Bayesian inference in a time series model provides exact, out-of-sample predictive distributions that fully and coherently incorporate parameter uncertainty. This study compares and evaluates Bayesian predictive distributions from alternative models, using as an illustration five alternative...

With the aim of constructing predictive distributions for daily returns, we introduce a new Markov normal mixture model in which the components are themselves normal mixtures. We derive the restrictions on the autocovariances and linear representation of integer powers of the time series in...

We employ a money-based early warning model in order to analyse the risk of a low inflation regime in the euro area, Japan and the US. The model specification allows for three different inflation regimes: Low, Medium and High inflation, while state transition probabilities vary over time as a...

We contribute to the empirical debate on the role of money in monetary policy by analysing the features of the relationship between money growth and inflation in a Bayesian Markov Switching framework for a set of four countries, the US, the UK, the Euro area and Japan, over an estimation period...

We employ a money-based early warning model in order to analyse the risk of a low inflation regime in the Euro Area, Japan and the US. The model specification allows for three different inflation regimes: "Low", "Medium" and "High" inflation, while state transition probabilities vary over time...