This paper develops the theoretical background for the Limited Information Bayesian Model Averaging (LIBMA). The proposed approach accounts for model uncertainty by averaging over all possible combinations of predictors when making inferences about the variables of interest, and it...

Do growth spells in Africa end because of bad realizations of the same factors that influence growth spells in the rest of the world, or because of different factors altogether? To answer this question, we examine determinants of growth spells in Africa and the rest of the world using Bayesian...

This paper revisits the cross-country growth empirics debate using a novel Limited Information Bayesian Model Averaging framework to address model uncertainty in the context of a dynamic growth model in panel data with endogenous regressors. Our empirical findings suggest that once model...

Do growth spells in Africa end because of bad realizations of the same factors that influence growth spells in the rest of the world, or because of different factors altogether? To answer this question, we examine determinants of growth spells in Africa and the rest of the world using Bayesian...

This paper extends the Bayesian Model Averaging framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using simulated data. Simulation...

Do growth spells in Africa end because of bad realizations of the same factors that influence growth spells in the rest of the world, or because of different factors altogether? To answer this question, we examine determinants of growth spells in Africa and the rest of the world using Bayesian...

Bayesian Model Averaging (BMA) provides a coherent mechanism to address the problem of model uncertainty. In this paper we extend the BMA framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information...