Small Sample Bias Propreties of the System GMM Estimator in Dynamic Panel Data Models

Kazuhiko Hayakawa

Hi-Stat Discussion Paper Series from Institute of Economic Research, Hitotsubashi University

Abstract: This paper examines analytically and experimentally why the system GMM estimator in dynamic panel data models is less biased than the first differencing or the level estimators even though the former uses more instruments. We find that the bias of the system GMM estimator is a weighted sum of the biases in opposite directions of the first differencing and the level estimator. We also find that an important condition for the system GMM estimator to have small bias is that the variances of the individual effects and the disturbances are almost of the same magnitude. If the variance of individual effects is much larger than that of disturbances, then all GMM estimators are heavily biased. To reduce such biases, we propose bias-corrected GMM estimators. On the other hand, if the variance of individual effects is smaller than that of disturbances, the system estimator has a more severe downward bias than the level estimator.

Date: 2005-04
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://hi-stat.ier.hit-u.ac.jp/research/discussion/2005/pdf/D05-82.pdf (application/pdf)

Related works:
Journal Article: Small sample bias properties of the system GMM estimator in dynamic panel data models (2007)
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: https://EconPapers.repec.org/RePEc:hst:hstdps:d05-82

Access Statistics for this paper

More papers in Hi-Stat Discussion Paper Series from Institute of Economic Research, Hitotsubashi University Contact information at EDIRC.
Bibliographic data for series maintained by Tatsuji Makino ().