On skewness and kurtosis of econometric estimators
Yong Bao () and
Econometrics Journal, 2009, vol. 12, issue 2, 232-247
We derive the approximate results for two standardized measures of deviation from normality, namely, the skewness and excess kurtosis coefficients, for a class of econometric estimators. The results are built on a stochastic expansion of the moment condition used to identify the econometric estimator. The approximate results can be used not only to study the finite sample behaviour of a particular estimator, but also to compare the finite sample properties of two asymptotically equivalent estimators. We apply the approximate results to the spatial autoregressive model and find that our results approximate the non-normal behaviours of the maximum likelihood estimator reasonably well. However, when the weights matrix becomes denser, the finite sample distribution of the maximum likelihood estimator departs more severely from normality and our results provide less accurate approximation. Copyright © 2009 The Author(s). Journal compilation © Royal Economic Society 2009
References: Add references at CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
http://www.blackwell-synergy.com/doi/abs/10.1111/j.1368-423X.2009.00289.x link to full text (text/html)
Access to full text is restricted to subscribers.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ect:emjrnl:v:12:y:2009:i:2:p:232-247
Ordering information: This journal article can be ordered from
Access Statistics for this article
Econometrics Journal is currently edited by Richard J. Smith, Oliver Linton, Pierre Perron, Jaap Abbring and Marius Ooms
More articles in Econometrics Journal from Royal Economic Society Contact information at EDIRC.
Series data maintained by Wiley-Blackwell Digital Licensing ().