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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...
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This note presents the R package bayesGARCH (Ardia, 2007) which provides functions for the Bayesian estimation of the …
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This paper analyzes the forecasting performance of an open economy DSGE model, estimated with Bayesian methods, for the … likelihood and two different Bayesian approaches, and traditional benchmark models, e.g. the random walk. The accuracy of point … present. We also critically examine the role of Bayesian model probabilities and other frequently used low …
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We define a dynamic and self-adjusting mixture of Gaussian Graphical Models to cluster financial returns, and provide a new method for extraction of nonparametric estimates of dynamic alphas (excess return) and betas (to a choice set of explanatory factors) in a multivariate setting. This...
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