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|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://www.cemfi.es/
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Theo Eicher & Chris Papageogiou & Adrian E Raftery, 2007. "Default Priors and Predictive Performance in Bayesian Model Averaging, with Application to Growth Determinants," Working Papers UWEC-2007-25-P, University of Washington, Department of Economics.
- Kleibergen, Frank & Zivot, Eric, 2003.
"Bayesian and classical approaches to instrumental variable regression,"
Journal of Econometrics,
Elsevier, vol. 114(1), pages 29-72, May.
- Frank Kleibergen & Eric Zivot, 1998. "Bayesian and Classical Approaches to Instrumental Variable Regression," Working Papers 0063, University of Washington, Department of Economics.
- 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.
- Kleibergen, F.R. & Zivot, E., 1998. "Bayesian and classical approaches to instrumental variable regression," Econometric Institute Research Papers EI 9835, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Frank Kleibergen & Eric Zivot, 2003. "Bayesian and Classical Approaches to Instrumental Variable Regression," Working Papers UWEC-2002-21-P, University of Washington, Department of Economics.
- Frank Kleibergen & Eric Zivot, 1998. "Bayesian and Classical Approaches to Instrumental Variables Regression," Econometrics 9812002, EconWPA.
When requesting a correction, please mention this item's handle: RePEc:cmf:wpaper:wp2010_1006. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Araceli Requerey)
If references are entirely missing, you can add them using this form.