A Monte Carlo Study of Growth Regressions

61 Pages Posted: 28 Jun 2007

See all articles by William R. Hauk

William R. Hauk

Independent

Romain T. Wacziarg

UCLA Anderson School of Management; National Bureau of Economic Research (NBER)

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Abstract

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.

Keywords: growth regressions, measurement error

Suggested Citation

Hauk, William R. and Wacziarg, Romain T., A Monte Carlo Study of Growth Regressions. Stanford University Graduate School of Business Research Paper No. 1831 (RI). Available at SSRN: https://ssrn.com/abstract=997147 or http://dx.doi.org/10.2139/ssrn.997147

William R. Hauk (Contact Author)

Independent

No Address Available

Romain T. Wacziarg

UCLA Anderson School of Management ( email )

110 Westwood Plaza
Los Angeles, CA 90095-1481
United States

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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