More efficient estimation under non-normality when higher moments do not depend on the regressors, using residual augmented least squares

Kyung So Im and Peter Schmidt

Journal of Econometrics, 2008, vol. 144, issue 1, 219-233

Abstract: Under normality, least squares is efficient. However, if the errors are not normal, we can gain efficiency from the assertion that higher moments do not depend on the regressors. In this paper, we show how the assumption that higher moments do not depend on the regressors can be exploited in a GMM framework, and we provide simple estimators that are asymptotically equivalent to the GMM estimators. These estimators can be calculated by linear regressions which have been augmented with functions of the least squares residuals.

Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (45)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304-4076(08)00007-9
Full text for ScienceDirect subscribers only

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: https://EconPapers.repec.org/RePEc:eee:econom:v:144:y:2008:i:1:p:219-233

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().