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A Monte Carlo Study of Growth Regressions Author info | Abstract | Publisher info | Download info | Related research | Statistics Lee, Angela Y. (Stanford U)
Aaker, Jennifer L. (U of South Carolina)
Using Monte Carlo simulations, this paper evaluates the bias properties of common estimators used in growth regressions derived from the Solow model. We explicitly allow for measurement error in the right-hand side variables, as well as country-specific effects that are correlated with the regressors. Our results suggest that using 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 coeffcients. The fixed-effects estimator and the Arellano-Bond estimator greatly overstate the speed of convergence under a wide variety of assumptions concerning the type and extent of measurement error, while between understates it somewhat. Finally, fixed effects and Arellano-Bond bias towards zero the slope estimates on the human and physical capital accumulation variables.
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Paper provided by Stanford University, Graduate School of Business in its series Research Papers with number
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Date of creation: Nov 2006Date of revision:
Handle: RePEc:ecl:stabus:1836r1Contact details of provider: Postal: Stanford University, Stanford, CA 94305-5015 Phone: (650) 723-2146 Fax: (650)725-6750 Email: Web page: http://gsbapps.stanford.edu/researchpapers/ More information through EDIRC
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