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 estimatorshave been found to produce large finite-sample biases when using the standard first-differenced estimator. These b...

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Bibliographic Details
Published inEconometric reviews Vol. 19; no. 3; pp. 321 - 340
Main Authors Blundell, Richard, Bond, Stephen
Format Journal Article
LanguageEnglish
Published Marcel Dekker, Inc 2000
Taylor and Francis Journals
SeriesEconometric Reviews
Subjects
Econometrics
GMM
Mathematical methods
panel data
Panel surveys
Production functions
Regression analysis
Returns to scale
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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 estimatorshave 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&Dperforming US manufacturing companies we find that the additional instruments used in our extended GMM estimator yield much more reasonable parameter estimates.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:0747-4938
1532-4168
DOI:10.1080/07474930008800475