This paper investigates the expectations hypothesis for the Japanese term structure of interest rates using vector error correction models with multiple structural breaks, focusing on how the breaks affect volatility, risk premium and speed of the adjustment toward the equilibrium. Using...

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 describes a procedure for constructing theory restricted prior distributions for BVAR models. The Bayes Factor, which is obtained without any additional computational effort, can be used to assess the plausibility of the restrictions imposed on the VAR parameter vector by competing...

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...

Judgment and decision making research overwhelmingly uses null hypothesis significance testing as the basis for statistical inference. This article examines an alternative, Bayesian approach which emphasizes the choice between two competing hypotheses and quantifies the balance of evidence...

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...

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 paper considers a vector autoregressive model or a vector error correction model with multiple structural breaks in any subset of parameters, using a Bayesian approach with Markov chain Monte Carlo simulation technique. The number of structural breaks is determined as a sort of model...