Grace Alinaitwe Makerere University Business School 10th ORSEA-15-17October 2014

1. Determinants of Economic Growth in Africa with emphasis on the role of financial markets using Bayesian Averaging of Classical Estimates Grace Alinaitwe Makerere University Business School 10th ORSEA-15-17October 2014

2. Outline

3. Motivation • Growth theories do not clearly specify the explanatory variables to include in the "true" regression. • The debate of whether finance leads or follows economic growth • A few studies have looked at determinants of economic growth using a Bayesian averaging of classical estimates • Negative, positive and none relationships have been found between economic growth and financial intermediaries.

4. Objectives

5. Contributions

6. Contributions … continued

7. Literature review

8. Data and sources

9. Data and sources … continued

10. Model specification • Bayesian Averaging of Classical Estimates • Posterior inclusion probability of a variable shows the importance of a certain variable in explaining the dependent variable • Important variables must have a higher posterior inclusion probability than their prior one.

11. BIC weights penalize large models and helps address the problem of colinearity in large models. • Expected model size equals 5, the prior inclusion probability is 5/14 = 0.3571

12. Bayesian Averaging of Classical Estimates The posterior model weights in the above equation are equal to the prior model weights times the Bayesian Information Criterion (BIC) developed by Schwarz (1978) divided by the sum of prior weights times the Bayesian Information Criterion of all possible models. Similar variables usually explain relatively less variation in the dependent variable and (BIC) implies less weight on such models.

13. Bayesian Averaging of Classical Estimates • BACE combines the averaging of estimates across models with classical ordinary least-squares (OLS) estimation. • Its advantages over model-averaging • requires the specification of only one prior hyper-parameter the expected model size k • estimates are calculated using only repeated OLS • This method takes into account all the possible models

14. Results

15. Robustness of the results

16. Conclusion

17. Conclusion … continued

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