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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. Outline. Motivation. - PowerPoint PPT Presentation
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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.
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.
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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
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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.
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
Variable posterior
prob
Posterior unconditional posterior conditional
Mean st. dev. Mean st. dev
FDI 1 0.0021 0.0002 0.0021 0.0002
Llgdp 0.4108 0.0136 0.0197 0.0332 0.0173
Lcgdp 0.2995 -0.0071 0.0142 -0.0239 0.0166
Popg 0.2792 -0.2317 0.4688 -0.83 0.5391
Fert 0.2608 -0.0016 0.0038 -0.0063 0.0051
INFL 0.1929 0.0001 0.0002 0.0003 0.0002
pcrdbc 0.1873 0.006 0.0172 0.0321 0.0272
Oil 0.1498 -0.0014 0.0048 -0.0091 0.0092
Scho 0.1214 0 0.0001 0.0001 0.0002
Lpop 0.1026 0.0002 0.0016 0.0015 0.0047
cbagdp 0.1004 0.0002 0.0086 0.0021 0.0271
Lpi 0.0872 0.0002 0.0043 0.0025 0.0144
Open 0.0855 -0.0001 0.0029 -0.0007 0.0099
Life 0.0844 0 0.002 -0.0001 0.0069
variable Kbar=3 Kbar=5 Kbar=7 Kbar=9 Kbar=11
prior
inclusion
probalility 0.2143 0.3571 0.5 0.6429 0.7857
FDI 1 1 1 1 1
Llgdp 0.3369 0.4108 0.4733 1 1
Popg 0.2071 0.2792 0.3352 1 1
Fert 0.1704 0.2608 0.3618 1 1
Lcgdp 0.1492 0.2995 0.4936 1 1
Pcrdbc 0.1197 0.1873 0.2656 1 1
INFL 0.1003 0.1929 0.2955 1 1
Oil 0.0743 0.1498 0.215 1 1
Scho 0.0706 0.1214 0.1702 1 1
Lpop 0.0557 0.1026 0.1573 1 1
Cbagdp 0.0509 0.1004 0.1514 1 1
Life 0.0492 0.0844 0.151 1 1
Lpi 0.0454 0.0872 0.1489 1 1
Open 0.0443 0.0855 0.1452 1 1