This paper highlights a problem in using the first-difference GMM panel data estimator
cross-country growth regressions. When the time series are persistent, the first-differenced
GMM estimator can be poorly behaved, since lagged levels of the series provide only
weak instruments for subsequent first-differences. Revisiting the work of Caselli,
Esquivel and Lefort (1996), we show that this problem may be serious in practice.
We suggest using a more efficient GMM estimator that exploits stationarity restrictions,
and this approach is shown to give more reasonable results than first-differenced
GMM in our estimation of an empirical growth model.