Panel Growth Regressions With General Predetermined Variables: Likelihood-Based Estimation And Bayesian Averaging
AbstractIn 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.
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Bibliographic InfoPaper provided by CEMFI in its series Working Papers with number wp2010_1006.
Date of creation: Jul 2010
Date of revision:
Dynamic panel estimation; growth regressions; Bayesian Model Averaging; weak instruments; maximum likelihood.;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
- O40 - Economic Development, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-08-28 (All new papers)
- NEP-ECM-2010-08-28 (Econometrics)
- NEP-FDG-2010-08-28 (Financial Development & Growth)
- NEP-ORE-2010-08-28 (Operations Research)
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