We propose a flexible stochastic framework for modeling the market share dynamics over time in a multiple markets setting, where firms interact within and between markets. Firms undergo stochastic idiosyncratic shocks, which contract their shares, and compete to consolidate their position by...

A seller dynamically sells a divisible good to a buyer. It is common knowledge that there are gains from trade and that the gains per unit are decreasing. Payoffs are interdependent as in Akerlof's market for lemons. The seller is informed about the good's quality. The buyer makes an offer in...

We study dynamic contracting with adverse selection and limited commitment. A firm (the principal) and a worker (the agent) interact for potentially infinitely many periods. The worker is privately informed about his productivity and the firm can only commit to short-term contracts. The ratchet...

We study the relative performance of disclosure and auditing in organizations. We consider the information transmission problem between two decision makers who take actions at dates 1 and 2 respectively. The first decision maker has private information about a state of nature that is relevant...

We examine the buyer-seller problem under different levels of commitment. The seller is informed of the quality of the good, which affects both his cost and the buyer's valuation, but the buyer is not. We characterize the allocations that can be achieved through mechanisms in which, unlike with...

This paper analyzes the optimal provision of incentives in a sequential testing context. In every period the agent can acquire costly information that is relevant to the principal's decision. Neither the agent's effort nor the realizations of his signals are observable. First, we assume that the...

The study of properties of mean functionals of random probability measures is an important area of research in the theory of Bayesian nonparametric statistics. Many results are known by now for random Dirichlet means but little is known, especially in terms of posterior distributions, for...

A Bayesian nonparametric methodology has been recently proposed in order to deal with the issue of prediction within species sampling problems. Such problems concern the evaluation, conditional on a sample of size n, of the species variety featured by an additional sample of size m. Genomic...

An important issue in survival analysis is the investigation and the modeling of hazard rates. Within a Bayesian nonparametric framework, a natural and popular approach is to model hazard rates as kernel mixtures with respect to a completely random measure. In this paper we provide a...

Bayesian nonparametric inference is a relatively young area of research and it has recently undergone a strong development. Most of its success can be explained by the considerable degree of flexibility it ensures in statistical modelling, if compared to parametric alternatives, and by the...