We propose a Bayesian Metropolis-Gibbs Monte Carlo Markov Chain (MCMC) algorithm to estimate parameters of a sample selection model in which the selected equation include a binary endogenous explanatory variable, using a three simultaneous equation model. We apply our methodology to...

Markov-switching models are usually specified under the assumption that all the parameters change when a regime switch occurs. Relaxing this hypothesis and being able to detect which parameters evolve over time is relevant for interpreting the changes in the dynamics of the series, for...

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample...

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the model which applies in each regime and the out-of-sample probability of a break...

We complete the development of a testing ground for axioms of discrete stochastic choice. Our contribution here is to develop new posterior simulation methods for Bayesian inference, suitable for a class of prior distributions introduced by McCausland and Marley (2013). These prior distributions...

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...

This paper introduces a formal method of combining expert and model density forecasts when the sample of past forecasts is unavailable. It works directly with the expert forecast density and endogenously delivers weights for forecast combination, relying on probability rules only. The empirical...

We present an estimation and forecasting method, based on a differential evolution MCMC method, for inference in GARCH models subjected to an unknown number of structural breaks at unknown dates. We treat break dates as parameters and determine the number of breaks by computing the marginal...

We complete the development of a testing ground for axioms of discrete stochastic choice. Our contribution here is to develop new posterior simulation methods for Bayesian inference, suitable for a class of prior distributions introduced by McCausland and Marley (2013). These prior distributions...

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...