The UN Millennium Development Goals have recognized poverty reduction as the main goal of global development policy. A comprehensive framework to evaluate the effectiveness of single policy measures and policy packages with respect to poverty reduction is still lacking, though. Policy evaluation...

This paper introduces a new measure of dependence or jointness among explanatory variables. Jointness is based on the joint posterior distribution of variables over the model space, thereby taking model uncertainty into account. By looking beyond marginal measures of variable importance,...

This paper considers the problem of model uncertainty associated with variable selection and specification of the spatial weight matrix in spatial growth regression models in general and growth regression models based on the matrix exponential spatial specification in particular. A natural...

This article is concerned with the estimation of linear regression models with uncertainty about the choice of the explanatory variables. We introduce the Stata commands bma and wals which implement, respectively, the exact Bayesian Model Averaging (BMA) estimator and the Weighted Average Least...

Pro-Poor Growth (PPG) is the vision of combining high growth rates with poverty reduction.Due to the myriad of possible determinants of growth and poverty a unique theoretical model for guiding empirical work on PPG is absent, though.Bayesian Model Averaging is a statistically robust framework...

Model uncertainty arises from uncertainty about correct economic theories, data issues and empirical specification problems. This paper investigates mutual dependence or jointness among variables in explaining the dependent variable. Jointness departs from univariate measures of variable...

In this study we evaluate the forecast performance of model averaged forecasts based on the predictive likelihood carrying out a prior sensitivity analysis regarding Zellner's g prior. The main results are fourfold: First the predictive likelihood does always better than the traditionally...

Ciccone and Jarocínski (2010) show that inference in Bayesian model averaging (BMA) can be highly sensitive to small changes in the dependent variable. In particular they demonstrate that the importance of growth determinants in explaining growth varies tremendously over different revisions of...

This paper explores the role that the imperfect knowledge of the structure of the economy plays in the uncertainty surrounding the effects of rule-based monetary policy on unemployment dynamics in the euro area and the US. We employ a Bayesian model averaging procedure on a wide range of models...

This paper explores the role that the imperfect knowledge of the structure of the economy plays in the uncertainty surrounding the effects of rule-based monetary policy on unemployment dynamics in the euro area and the US. We employ a Bayesian model averaging procedure on a wide range of models...