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Cited by 1,681

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

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

Blundell, Richard, Bond, Stephen·1999·Working Paper Series
DOI 10.1920/wp.ifs.1999.9904
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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. © 2000, Taylor & Francis Group, LLC.

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Metadata

Authors
Blundell, R., & Bond, S.
Venue
Working Paper Series
Publisher
Institute for Fiscal Studies
Published
1999-02-01
ISSN
  • 1369-4685 (ISSN-L)
  • 1742-0415
DOI
10.1920/wp.ifs.1999.9904
OpenAlex
W2052546656
Cited by
1,681
References
3
Type
report
Language
en
Open access
closed

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CoChat. (2026). Citation check: GMM estimation with persistent panel data: an application to production functions. https://app.cochat.ai/papers/10.1920/wp.ifs.1999.9904
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Blundell, R., & Bond, S. (1999). GMM estimation with persistent panel data: an application to production functions. Working Paper Series. https://doi.org/10.1920/wp.ifs.1999.9904

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.