Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator.
This chapter reviews developments to improve on the poor performance of the standard GMM estimator for highly autoregressive panel series. It considers the use of the "system" GMM estimator that relies on relatively mild restrictions on the initial condition process. This system GMM estima...
| 主要な著者: | , , |
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| フォーマット: | Working paper |
| 言語: | English |
| 出版事項: |
Institute for Fiscal Studies
2000
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| _version_ | 1826267303987118080 |
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| author | Blundell, R Bond, S Windmeijer, F |
| author_facet | Blundell, R Bond, S Windmeijer, F |
| author_sort | Blundell, R |
| collection | OXFORD |
| description | This chapter reviews developments to improve on the poor performance of the standard GMM estimator for highly autoregressive panel series. It considers the use of the "system" GMM estimator that relies on relatively mild restrictions on the initial condition process. This system GMM estimator encompasses the GMM estimator based on the non-linear moment conditions available in the dynamic error components model and has substantial asymptotic efficiency gains. Simulations, that include weakly exogenous covariates, find large finite sample biases and very low precision for the standard first differenced estimator. The use of the system GMM estimator not only greatly improves the precision but also greatly reduces the finite sample bias. An application to panel production function data for the US is provided and confirms these theoretical and experimental findings. |
| first_indexed | 2024-03-06T20:52:07Z |
| format | Working paper |
| id | oxford-uuid:37f179d0-f5fe-49d7-b17c-8732f7604cbf |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T20:52:07Z |
| publishDate | 2000 |
| publisher | Institute for Fiscal Studies |
| record_format | dspace |
| spelling | oxford-uuid:37f179d0-f5fe-49d7-b17c-8732f7604cbf2022-03-26T13:47:03ZEstimation in dynamic panel data models: improving on the performance of the standard GMM estimator.Working paperhttp://purl.org/coar/resource_type/c_8042uuid:37f179d0-f5fe-49d7-b17c-8732f7604cbfEnglishDepartment of Economics - ePrintsInstitute for Fiscal Studies2000Blundell, RBond, SWindmeijer, FThis chapter reviews developments to improve on the poor performance of the standard GMM estimator for highly autoregressive panel series. It considers the use of the "system" GMM estimator that relies on relatively mild restrictions on the initial condition process. This system GMM estimator encompasses the GMM estimator based on the non-linear moment conditions available in the dynamic error components model and has substantial asymptotic efficiency gains. Simulations, that include weakly exogenous covariates, find large finite sample biases and very low precision for the standard first differenced estimator. The use of the system GMM estimator not only greatly improves the precision but also greatly reduces the finite sample bias. An application to panel production function data for the US is provided and confirms these theoretical and experimental findings. |
| spellingShingle | Blundell, R Bond, S Windmeijer, F Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator. |
| title | Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator. |
| title_full | Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator. |
| title_fullStr | Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator. |
| title_full_unstemmed | Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator. |
| title_short | Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator. |
| title_sort | estimation in dynamic panel data models improving on the performance of the standard gmm estimator |
| work_keys_str_mv | AT blundellr estimationindynamicpaneldatamodelsimprovingontheperformanceofthestandardgmmestimator AT bonds estimationindynamicpaneldatamodelsimprovingontheperformanceofthestandardgmmestimator AT windmeijerf estimationindynamicpaneldatamodelsimprovingontheperformanceofthestandardgmmestimator |