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GMM Estimation with persistent panel data: an application to production functions

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 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Econometric Reviews Taylor & Francis

GMM Estimation with persistent panel data: an application to production functions

Econometric Reviews , Volume 19 (3): 20 – Jan 1, 2000
20 pages

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References (14)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1532-4168
eISSN
0747-4938
DOI
10.1080/07474930008800475
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Econometric ReviewsTaylor & Francis

Published: Jan 1, 2000

Keywords: panel data;; GMM;; production functions

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