Midwest Macro Conference

Abstract This paper takes a fresh look into Africa's growth experience by using the Bayesian Model Averaging (BMA) methodology. BMA enables us to consider a large number of potential explanatory variables and sort out which of these variable can e¤ectively explain Africa's growth experience. Posterior coe¢ cient estimates reveal that key engines of growth in Africa are substantially di¤erent from those in the rest of the world. More precisely, it is shown that mining, primary exports and initial primary education exerted di¤erential e¤ect on African growth. These results are examined in relation to the existing literature. JEL Classi…cation: O40, O47. Keywords: Africa, growth determinants, model uncertainty, Bayesian Model Averaging (BMA). We thank the editor Steven Durlauf and an anonymous referee for valuable comments and suggestions. We also thank seminar participants a

75 Years of Development Research Conference

Abstract This paper takes a fresh look into Africa's dismal growth performance by using the Bayesian Model Averaging (BMA) methodology. We estimate the posterior probability of a large number of potential explanatory variables and cross-country regression models. In large, we Þnd that determinants of growth in Africa are strikingly different from the rest of the world. In addition, growth regression models that best explain global growth do poorly in explaining African growth, and conversely

Comparison of the Forecasting Properties of K-Class Estimators.

Ph.D.EconomicsUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/156892/1/6606645.pd

A lagged dependent variable, autocorrelated disturbances, and unit root tests - peculiar OLS bias properties - a pedagogical note

The paper provides applied econometricians with a useful insight into the interaction between lagged dependent variables and autocorrelated disturbances. More specifically, the paper explains heuristically why, how and when the bias of the OLS estimator of the coefficient of a lagged dependent variable can be smaller when the disturbances are autocorrelated than when they are NID. It also explains why and how the powers and sizes of some of the unit root tests are distorted by AR(1) and MA(1) disturbances. The results should be of interest to applied econometricians using vector autoregressive or error-correction models as well.