A Monte Carlo study of growth regressions |
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Authors: | William R Hauk Jr Romain Wacziarg |
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Institution: | (1) Department of Economics, Moore School of Business, University of South Carolina, 1705 College St., Columbia, SC 29208, USA;(2) UCLA Anderson School of Management, 110 Westwood Plaza, Los Angeles, CA 90095, USA;(3) NBER, Cambridge, MA, USA;(4) CEPR, London, UK |
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Abstract: | Using Monte Carlo simulations, this paper evaluates the bias properties of estimators commonly used to estimate growth regressions
derived from the Solow model. We explicitly allow for measurement error, country-specific fixed effects and regressor endogeneity.
An OLS estimator applied to a single cross-section of variables averaged over time (the between estimator) performs best in
terms of the extent of bias on each of the estimated coefficients. Fixed-effects and the Arellano–Bond GMM estimator overstate
the speed of convergence under a wide variety of assumptions, while the between estimator understates it. Finally, fixed effects
and Arellano–Bond bias towards zero the slope estimates on the human and physical capital accumulation variables, while the
between estimator and the Blundell–Bond system GMM estimator bias these coefficients upwards.
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Keywords: | Growth regressions Measurement error System-GMM |
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