Purpose - When the probability of each model is known, a natural idea is to select the most probable model. However, in many practical situations, the exact values of these probabilities are not known; only the intervals that contain these values are known. In such situations, a natural idea is...

It has been shown that, despite being consistent and in some cases efficient, maximum pseudo-likelihood (MPL) estimation for copula models overestimates the level of dependence, especially for small samples with a low level of dependence. This is especially relevant in finance and insurance...

We study the application of approximate mean field variational inference algorithms to Bayesian panel VAR models in which an exchangeable prior is placed on the dynamic parameters and the residuals follow either a Gaussian or a Student-t distribution. This reduces the estimation time of possibly...

The existing literature on large dynamic factor models invariably assumes that the cross sectional covariance matrix is diagonal. This is due to the curse of dimensionality which means that many parameters need to be estimated for large data sets. This paper introduces a novel maximum likelihood...

The existing literature on large dynamic factor models invariably assumes that the cross sectional covariance matrix is diagonal. This is due to the curse of dimensionality which means that many parameters need to be estimated for large data sets. This paper introduces a novel maximum likelihood...