Working Paper: CEPR ID: DP3048
Authors: Stephen R. Bond; Anke Hoeffler; Jonathan Temple
Abstract: This Paper highlights a problem in using the first-differenced GMM panel data estimator to estimate 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.
Keywords: convergence; generalized method of moments; growth; weak instruments
JEL Codes: O41; O47
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| first-differenced GMM estimator (C51) | biased estimates of the coefficient on the lagged dependent variable (C51) |
| high persistence of output (E23) | weak correlation between lagged levels and subsequent first differences (C22) |
| system GMM estimator (C51) | more plausible estimates (C51) |
| investment rates (G31) | steady-state level of per capita GDP (P24) |