It is well known that a wide class of bayesian nonparametric priors lead to the representation of the distribution of the observable variables as a mixture density with an infinite number of components, and that such a representation induces a clustering structure in the observations. However,...

Sparse models, though long preferred and pursued by social scientists, can be ineffective or unstable relative to large models, for example, in economic predictions (Giannone et al., 2021). To achieve sparsity for economic interpretation while exploiting big data for superior empirical...

The full Bayesian treatment of error component models typically relies on data augmentation to produce the required inference. Never stricly necessary a direct approach is always possible though not necessarily practical. The mechanics of direct sampling are outlined and a template for including...

In a Bayesian analysis, different models can be compared on the basis of the expected or marginal likelihood they attain. Many methods have been devised to compute the marginal likelihood, but simplicity is not the strongest point of most methods. At the same time, the precision of methods is...

In this paper we introduce a stochastic network formation model where agents choose both actions and links. Neighbors in the network benefit from each other's action levels through local complementarities and there exists a global interaction effect reflecting a strategic substitutability in...

Recently, a large body of academic literature has focused on the area of stable distributions and their application potential for improving our understanding of the risk of hedge funds. At the same time, research has sprung up on standard Bayesian methods applied to hedge fund evaluation. Little...

We develop a new method to sample from posterior distributions in hierarchical models without using Markov chain Monte Carlo. This method, which is a variant of importance sampling ideas, is generally applicable to high-dimensional models involving large data sets. Samples are independent, so...