Bayesian Model Averaging (BMA) represents an internally consistent approach to conducting inference when faced with model uncertainty. The combination of continued expansion in compting power alongside improvements in the design of Markov Chain Monte Carlo algorithms has lead to an expansion in the use of BMA across a range of areas including, finance and growth. This site collects a number of outputs from our work on BMA. providing access to recent papers, an easy to use BMA software, and a course in BMA that can be tailored to individuals needs. We have been awarded a grant by CreMic to develop a full proposal on Bayesian Semi-Parametric Approaches to Robust Inference Datasets

i. Determinants of Long-Term Growth: A Bayesian Averaging of Classical
Estimates (BACE) Approach.

ii. Jointness of Growth Determinants. Weeks, Forthcoming Journal of Applied Econometrics.

iii. Robust Growth Determinants.

iv. Model Averaging.