The paper proposes a technique to jointly tests for groupings of unknown size in the cross sectional dimension of a panel and estimates the parameters of each group, and applies it to identifying convergence clubs in income per-capita. The approach uses the predictive density of the data,...

The paper proposes a technique to test jointly for groupings of unknown size in the cross-sectional dimension of a panel and estimates the parameters of each group, applying it to identifying convergence clubs in income per-capita. The approach uses the predictive density of the data,...

We study the issue of income convergence across countries and regions with a Bayesian model which allows us to use information in an efficient and flexible way. We argue that the very slow convergence rates to a common level of per-capita income found, for example, by Barro and Sala-i-Martin, is...

The paper proposes a technique to jointly test for groupings of unknown size in the cross sectional dimension of a panel and estimates the parameters of each group, and applies it to identifying convergence clubs in income per-capita. The approach uses the predictive density of the data,...

We consider a set of potentially misspecified structural models, geometrically combine their likelihood functions, and estimate the parameters using composite methods. In a Monte Carlo study, composite estimators dominate likelihood-based estimators in mean squared error and composite models are...

We consider a set of potentially misspecified structural models, geometrically combine their likelihood functions, and estimate the parameters using composite methods. In a Monte Carlo study, composite estimators dominate likelihood-based estimators in mean squared error and composite models are...