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...
| Huvudupphovsmän: | , , |
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| Materialtyp: | Working paper |
| Språk: | English |
| Publicerad: |
CEPR
2001
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| _version_ | 1826285313478098944 |
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| author | Bond, S Hoeffler, A Temple, J |
| author_facet | Bond, S Hoeffler, A Temple, J |
| author_sort | Bond, S |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T01:26:58Z |
| format | Working paper |
| id | oxford-uuid:924e6dcd-e9c2-4c05-809e-57cc6ab8dc76 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T01:26:58Z |
| publishDate | 2001 |
| publisher | CEPR |
| record_format | dspace |
| spelling | oxford-uuid:924e6dcd-e9c2-4c05-809e-57cc6ab8dc762022-03-26T23:24:31ZGMM Estimation of Empirical Growth Models.Working paperhttp://purl.org/coar/resource_type/c_8042uuid:924e6dcd-e9c2-4c05-809e-57cc6ab8dc76EnglishDepartment of Economics - ePrintsCEPR2001Bond, SHoeffler, ATemple, JThis 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. |
| spellingShingle | Bond, S Hoeffler, A Temple, J GMM Estimation of Empirical Growth Models. |
| title | GMM Estimation of Empirical Growth Models. |
| title_full | GMM Estimation of Empirical Growth Models. |
| title_fullStr | GMM Estimation of Empirical Growth Models. |
| title_full_unstemmed | GMM Estimation of Empirical Growth Models. |
| title_short | GMM Estimation of Empirical Growth Models. |
| title_sort | gmm estimation of empirical growth models |
| work_keys_str_mv | AT bonds gmmestimationofempiricalgrowthmodels AT hoefflera gmmestimationofempiricalgrowthmodels AT templej gmmestimationofempiricalgrowthmodels |