Panel Growth Regressions With General Predetermined Variables: Likelihood-Based Estimation And Bayesian Averaging
In this paper I estimate empirical growth models simultaneaously considering endogenous regressors and model uncertainty. In order to apply Bayesian methods such as Bayesian Model Averaging (BMA) to dynamic panel data models with predetermined or endogenous variables and fixed effects, I propose a likelihood function for such models. The resulting maximum likelihood estimator can be interpreted as the LIML counterpart of GMM estimators. Via Monte Carlo simulations, I conclude that the finite-sample performance of the proposed estimator is better than that of the commonly-used standard GMM. In contrast to the previous consensus in the empirical growth literature, empirical results indicate that once endogeneity and model uncertainty are accounted for, the estimated convergence rate is not significantly different from zero. Moreover, there seems to be only one variable, the investment ration, that causes long-run economic growth.
|Date of creation:||Jul 2010|
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- Frank Kleibergen & Eric Zivot, 2003.
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- Frank Kleibergen & Eric Zivot, 1998. "Bayesian and Classical Approaches to Instrumental Variable Regression," Discussion Papers in Economics at the University of Washington 0063, Department of Economics at the University of Washington.
- Frank Kleibergen & Eric Zivot, 1998. "Bayesian and Classical Approaches to Instrumental Variables Regression," Econometrics 9812002, EconWPA.
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- Theo Eicher & Chris Papageogiou & Adrian E Raftery, 2007.
"Default Priors and Predictive Performance in Bayesian Model Averaging, with Application to Growth Determinants,"
UWEC-2007-25-P, University of Washington, Department of Economics.
- Theo S. Eicher & Chris Papageorgiou & Adrian E. Raftery, 2011. "Default priors and predictive performance in Bayesian model averaging, with application to growth determinants," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(1), pages 30-55, January/F.
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