Bayesian Testing in Cointegration Models using the Jeffreys' Prior

We develop a Bayesian cointegration test statistic that can be used under a Jeffreys' prior. The test statistic is equal to the posterior expectation of the classical score statistic. Under the assumption of a full rank value of the long run multiplier the test statistic is a random variable with a chi-squared distribution. We evaluate whether the value of the test statistic under the restriction of cointegration is a plausible realization from its distribution under the encompassing, full rank model. We provide the posterior simulator that is needed to compute the test statistic. The simulator utilizes the invariance properties of the Jeffreys' prior such that the parameter drawings from a suitably rescaled model can be used. The test statistic can straightforwardly be extended to a more general model setting. For example, we show that structural breaks in the constant or trend and general mixtures of normal disturbances can be modelled, because conditional on some latent parameters all derivations still hold. We apply the Bayesian cointegration statistic to the Danish dataset of Johansen and Juselius (1990) and to four artificial examples to illustrate the use of the statistic as a diagnostic tool.

Semiparametric posterior limits

We review the Bayesian theory of semiparametric inference following Bickel and Kleijn (2012) and Kleijn and Knapik (2013). After an overview of efficiency in parametric and semiparametric estimation problems, we consider the Bernstein-von Mises theorem (see, e.g., Le Cam and Yang (1990)) and generalize it to (LAN) regular and (LAE) irregular semiparametric estimation problems. We formulate a version of the semiparametric Bernstein-von Mises theorem that does not depend on least-favourable submodels, thus bypassing the most restrictive condition in the presentation of Bickel and Kleijn (2012). The results are applied to the (regular) estimation of the linear coefficient in partial linear regression (with a Gaussian nuisance prior) and of the kernel bandwidth in a model of normal location mixtures (with a Dirichlet nuisance prior), as well as the (irregular) estimation of the boundary of the support of a monotone family of densities (with a Gaussian nuisance prior).Comment: 47 pp., 1 figure, submitted for publication. arXiv admin note: substantial text overlap with arXiv:1007.017

A dichotomy in dust-jet orientation in radio galaxies

We have analyzed the position angle (PA) differences between radio jets and dust distributions in the centers of Fanaroff & Riley Type 1 (FRI) radio galaxies. We model the observed PA differences to infer the three-dimensional relative orientation of jet and dust. Our main conclusion is that there is a dichotomy in dust-jet-galaxy orientation both in projection and in three-dimensional space. The orientation dichotomy can explain the contradictory results obtained in previous studies. We briefly mention scenarios that might explain the dichotomy.Comment: 2 pages, 2 figures, to appear in "The Interplay among Black Holes, Stars and ISM in Galactic Nuclei", IAU Symposium 222, eds. Th. Storchi Bergmann, L.C. Ho & H.R. Schmit