We consider the dynamic factor model where the loading matrix, the dynamic factors and the disturbances are treated as latent stochastic processes. We present empirical Bayes methods that enable the efficient shrinkage-based estimation of the loadings and the factors. We show that our estimates...

This paper shows that the parsimoniously time-varying methodology of Callot and Kristensen (2015) can be applied to factor models. We apply this method to study macroeconomic instability in the US from 1959:1 to 2006:4 with a particular focus on the Great Moderation. Models with parsimoniously...

We investigate the presence of international business cycles in macroeconomic aggregates (output, consumption, investment) using a panel of 60 countries over the period 1961 - 2014. The paper presents a Bayesian stochastic factor selection approach for dynamic factor models with predetermined...

Determining a plausible number of components in a factor model is a nontrivial issue in case of weak data, sparse model restrictions and diffuse prior information. We discuss the issue of structural parametric identification in a static factor model and introduce orthogonal restrictions which...

This paper examines the long-run trends in per-capita income across the US states (1929-2005). Our analysis advocates and implements a Vector Error Correction Model (VECM), in order to investigate whether disparities in per-capita income embody a stable long-run relation. The empirical...

Information and Communication Technologies (ICT) have become important tools to promote a variety of public goals and policies. In the past years much attention has been given to the expected social benefits from deploying ICT in different fields (transportation, education, public participation...

We assess the predictive ability of 15 economic uncertainty measures in a real-time out-of-sample forecasting exercise for the quantiles of The Conference Board's coincident economic index and its components (industrial production, employment, personal income, and manufacturing and trade sales)....

A Bayesian dynamic compositional model is introduced that can deal with combining a large set of predictive densities. It extends the mixture of experts and the smoothly mixing regression models by allowing for combination weight dependence across models and time. A compositional model with...

In several scientific fields, like bioinformatics, financial and macro-economics, important theoretical and practical issues exist that involve multimodal data distributions. We propose a Bayesian approach using mixtures distributions to approximate accurately such data distributions. Shape and...

A flexible predictive density combination model is introduced for large financial data sets which allows for dynamic weight learning and model set incompleteness. Dimension reduction procedures allocate the large sets of predictive densities and combination weights to relatively small sets....