We propose in this paper a likelihood-based framework forcointegration analysis in panels of a fixed number of vector errorcorrection models. Maximum likelihood estimators of thecointegrating vectors are constructed using iterated GeneralizedMethod of Moments estimators. Using these estimators...

Exchange rate returns are fat-tailed distributed. We provide evidence that the apparent non-normality derives from the behavior of macroeconomic fundamentals. Economic and probabilistic arguments are offered for such a relationship. Empirical support is given by testing against normality and...

This paper is the first attempt to assess the impact of official FOREX interventions of the three major central banks in terms of the dynamics of the currency components of the major exchange rates (EUR/USD and YEN/USD) over the period 1989-2003. We identify the currency components of the mean...

This paper uses Reuters exchange rate data to investigate thecontributions to the price discovery process by individual banks in theforeign exchange market. We propose multivariate time series models aswell as models in tick time to study the dynamic relations between thequotes of individual...

This paper documents the existence of large structural breaks in the unconditional correlations among the British pound, Norwegian krone, Swedish krona, Swiss franc, and euro exchange rates (against the US dollar) during the period 1994-2003. Using the framework of dynamic conditional...

In this paper we replace the Gaussian errors in the standard Gaussian, linear state space model with stochastic volatility processes. This is called a GSSF-SV model. We show that conventional MCMC algorithms for this type of model are ineffective, but that this problem can be removed by...

We propose a Bayesian infinite hidden Markov model to estimate time- varying parameters in a vector autoregressive model. The Markov structure allows for heterogeneity over time while accounting for state-persistence. By modelling the transition distribution as a Dirichlet process mixture model,...

We develop a vector autoregressive model with time variation in the mean and the variance. The unobserved time-varying mean is assumed to follow a random walk and we also link it to long-term Consensus forecasts, similar in spirit to so called democratic priors. The changes in variance are...

This paper proposes a novel approach to introduce time-variation in structural parameters of DSGE models. Structural parameters are allowed to evolve over time via an observation-driven updating equation. The estimation of the resulting DSGE model can be easily performed by maximum likelihood...