A Bayesian Analysis of a Variance Decomposition for Stock Returns We apply Bayesian methods to study a common VAR-based approach for decomposing the variance of excess stock returns into components reflecting news about future excess stock returns, future real interest rates, and future dividends. We develop a new prior elicitation strategy which involves expressing beliefs about the components...

this study investigated the dynamic relationship between money, prices and output in a multivariate structure of casualty analysis in iran for the two period of 1969 to 2012 (entire period) and 1989 to 2012 (sub-period). this statistical framework has been projected for situations where causal links may have changed over the sample period. results of a three-variable vector error correction mod...

This paper points out a conceptual difficulty in using a variance decomposition to assess the quantitative importance of news shocks. A variance decomposition will attribute to news shocks movements in endogenous variables driven both by news about future exogenous fundamentals that has yet to materialize (what I call “pure news”) as well as movements driven by realized changes in fundamentals ...

We propose here an interpolation method based on a decomposition of the data in largeand small-scale variation. This decomposition was performed using a two-way directional decomposition, similar to the decomposition used by Cressie in his median-polish kriging (1993), though we applied decomposition by means instead of medians. We considered the effects isolated by the decomposition as associa...

The Continuous Assessment of Interpersonal Dynamics (CAID) is an observational coding method that enables continuous tracking warmth and dominance in both members a dyad as interaction unfolds. Research using this tool has revealed dynamic patterns relevant to psychopathology psychotherapy, suggesting considerable potential for clinical assessment research. However, CAID data are sensitive vari...

Ensembles of classifiers represent one of the main research directions in machine learning. Two main theories are invoked to explain the success of ensemble methods. The first one consider the ensembles in the framework of large margin classifiers, showing that ensembles enlarge the margins, enhancing the generalization capabilities of learning algorithms. The second is based on the classical b...

This paper proposes a spatially adaptive statistical model for wavelet image coefficients in order to perform image de-noising. The wavelet coefficients are modelled as zero-mean Gaussian random variables with high local correlation. This model is developed in a Bayesian framework, where a Maximum Likelihood (ML) estimator evaluates the variance of the blocks to which the wavelet subbands have ...