This paper introduces Bayesian inference in a Markov switching partial cointegration model. The partial cointegration allows the cointegration relationships to be switched on and off depending on the regime, unlike conventional cointegration analysis that assumes linear adjustment toward...

This paper is concerned with the problems of posterior simulation and model choice for Poisson panel data models with multiple random effects. Efficient algorithms based on Markov Chain Monte Carlo methods for sampling the posterior distribution are developed. A new parameterization of the...

This paper is concerned with statistical inference in multinomial probit, multinomial-$t$ and multinomial logit models. New Markov chain Monte Carlo (MCMC) algorithms for fitting these models are introduced and compared with existing MCMC methods. The question of parameter identification in the...

In this essay, I argue about the relevance and the ultimate unity of the Bayesian approach in a neutral and agnostic manner. My main theme is that Bayesian data analysis is an effective tool for handling complex models, as proven by the increasing proportion of Bayesian studies in the applied...

This introduction to Bayesian statistics presents the main concepts as well as the principal reasons advocated in favour of a Bayesian modelling. We cover the various approaches to prior determination as well as the basis asymptotic arguments in favour of using Bayes estimators. The testing...

This paper intends to investigate the duration dependent feature of Taiwan's business cycles. The constant Markov switching model is revised to take account of the duration dependent feature. The most innovative findings herein are that there is no duration dependence for contraction for the...

The duration dependence of stock market cycles has been investigated using the Markov switching model where the market conditions are unobservable. In conventional modeling, restrictions are imposed such that the transition probability is a monotonic function of duration, which is truncated at a...

We develop a Markov Switching model for inflation with time-varying transition probabilities. Inflation is characterized by two regimes (high and low inflation) and the probability of regime changes depends on money growth. Using Bayesian techniques, we apply the model to the euro area, Germany,...

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...