Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator

Richard Blundell (), Stephen Roy Bond and Frank Windmeijer ()

No W00/12, IFS Working Papers from Institute for Fiscal Studies

Abstract: 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.

Date: 2000-06
View citations in EconPapers

Downloads: (external link)
http://www.ifs.org.uk/wps/wp0012.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:ifs:ifsewp:00/12

Ordering information: This working paper can be ordered from
The Institute for Fiscal Studies 7 Ridgmount Street LONDON WC1E 7AE

Access Statistics for this paper

More papers in IFS Working Papers from Institute for Fiscal Studies
Address: The Institute for Fiscal Studies 7 Ridgmount Street LONDON WC1E 7AE
Series data maintained by Emma Hyman ().