In this paper we test for mean reversion in the Nordic stock markets using monthly nominal data 1947-1998. By simply account for the heteroscedasticity of the data with a regime-switching model of normal distributions and taking estimation bias into account via a Bayesian approach we can find no...

The failure to describe the time series behaviour of most realexchange rates as temporary deviations from fixedlong-term means may be due to time variation of the equilibriathemselves, see Engel (2000). We implement thisidea using an unobserved components model and decompose theobservations on...

The failure to describe the time series behaviour of most real exchange rates as temporary deviations from fixed long-term means may be due to time variation of the equilibria themselves, see Engel (2000). We implement this idea using an unobserved components model and decompose the observations...

The failure to describe the time series behaviour of most real exchange rates as temporary deviations from fixed long-term means may be due to time variation of the equilibria themselves, see Engel (2000). We implement this idea using an unobserved components model and decompose the observations...

In this paper we test for mean reversion in the Nordic stock markets using monthly nominal data 1947-1998. By simply account for the heteroscedasticity of the data with a regime-switching model of normal distributions and taking estimation bias into account via a Bayesian approach we can find no...

The problem of testing a set of restrictions R(q)=0 in a complex hierarchical model is considered. We propose a different approach from the standard PO ratio test, which can be viewed as the Bayesian analogous to the classical Wald type test. With respect to the PO ratio, it has the advantage of...

The failure to describe the time series behaviour of most real exchange rates as temporary deviations from fixed long-term means may be due to time variation of the equilibria themselves, see Engel (2000). We implement this idea using an unobserved components model and decompose the observations...

Space-varying regression models are generalizations of standard linear models where the regression coefficients are allowed to change in space. The spatial structure is specified by a multivariate extension of pairwise difference priors, thus enabling incorporation of neighboring structures and...