GMM Estimation of Empirical Growth Models.
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 su...
| Main Authors: | , , |
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| Format: | Working paper |
| Language: | English |
| Published: |
CEPR
2001
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| Summary: | 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. |
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