In this paper, we use Bayesian nonparametric learning to estimate the skill of actively managed mutual funds and also to estimate the population distribution for this skill. A nonparametric hierarchical prior, where the hyperprior distribution is unknown and modeled with a Dirichlet process...

To measure contagion empirically, we propose using a Bayesian time-varying coefficient model estimated with Markov ChainMonte Carlo methods. The proposed measure works in the joint presence of heteroskedasticity and omitted variables and does not require knowledge of the timing of the crisis. It...

Structural VARs have been extensively used in empirical macroeconomics during the last two decades, particularly in analyses of monetary policy. Existing Bayesian procedures for structural VARs are at best confined to a severly limited handling of cointegration restrictions. This paper extends...

This paper proposes a methodology that combines the use of Schwarz's BIC in subset autoregression and subset transfer function identification along with the posterior odds ratio test developed by Poskitt & Tremayne (1987) in the context of testing for Granger-causality and cointegration tests....

We show that the use of prior information derived from former empirical findings and/or subject matter theory regarding the lag structure of the observable variables together with an AR process for the error terms can produce univariate and single equation models that are intuitively appealing,...

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...

Social conformity can spread social norms and behaviors through a society. This research examines such a process geographically for conformity with the norm that good citizens should vote, which in turn affects voter turnout. A mathematical model for this process is developed, and predictions...

Copula models have become one of the most widely used tools in the applied modelling of multivariate data. Similarly, Bayesian methods are increasingly used to obtain efficient likelihood-based inference. However, to date, there has been only limited use of Bayesian approaches in the formulation...