We explore the use of univariate low-frequency filters in macroeconomic forecasting. This amounts to targeting only specific fluctuations of the time series of interest. We show through simulations that such approach is warranted and, using US data, we confirm empirically that consistent gains...

This discussion paper resulted in a publication in the 'Journal of Economic Dynamics and Control' (forthcoming).<P> This paper develops a testing framework for comparing the predictive accuracy of copula-based multivariate density forecasts, focusing on a specific part of the joint distribution....</p>

This paper develops a testing framework for comparing the predictive accuracy of competing multivariate density forecasts with different predictive copulas, focusing on specific parts of the copula support. The tests are framed in the context of the Kullback–Leibler Information Criterion,...

This paper considers Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. I show that by acknowledging the correlation structure in the predictors can improve forecasts over existing popular Bayesian variable selection algorithms.

This paper proposes a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models. In contrast to the parametric literature the return distribution can display general forms of asymmetry and thick tails. An infinite mixture of multivariate normals is given a...