GMM Estimation with Persistent Panel Data: An Application to Production Functions.
This paper considers the estimation of Cobb-Douglas production functions using panel data covering a large sample of companies observed for a small number of time periods. GMM estimators have been found to produce large finite-sample biases when using the standard first-differenced estimator. These...
| Main Authors: | , |
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| Format: | Journal article |
| Language: | English |
| Published: |
2000
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| Summary: | This paper considers the estimation of Cobb-Douglas production functions using panel data covering a large sample of companies observed for a small number of time periods. GMM estimators have been found to produce large finite-sample biases when using the standard first-differenced estimator. These biases can be dramatically reduced by exploiting reasonable stationarity restrictions on the initial conditions process. Using data for a panel of R&D-performing; US manufacturing companies we find that the additional instruments used in our extended GMM estimator yield much more reasonable parameter estimates. |
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