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A Monte Carlo Study of Growth Regressions

  • William R. Hauk
  • Romain Wacziarg

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 coefficients. 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 National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0296.

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Date of creation: Jan 2004
Date of revision:
Handle: RePEc:nbr:nberte:0296
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