A Minimax Bias Estimator for OLS Variances Under Heteroskedasticity

Analytic evaluation of heteroskedasticity consistent covariance matrix estimates (HCCME) is difficult because of the complexity of the formulae currently available. We obtain new analytic formulae for the bias of a class of estimators of the covariance matrix of OLS in a standard linear regression model. These formulae provide substantial insight into the properties and performance characteristics of these estimators. In particular, we find a new estimator which minimizes the maximum possible bias and improves substantially on the standard Eicker-White estimate

MoreLess

Year of publication:	2014
Authors:	Ahmed, Mumtaz
Other Persons:	Zaman, Asad (contributor)
Publisher:	[2014]: [S.l.] : SSRN
Subject:	Kleinste-Quadrate-Methode \| Least squares method \| Heteroskedastizität \| Heteroscedasticity \| Schätztheorie \| Estimation theory \| Systematischer Fehler \| Bias

freely available

Full text |

More access options

doi.org

Check Google Scholar

In German libraries (KVK)

I need help

Which error do you want to report?

Report: URL of this record is broken

Please note: Sometimes more than one URL is available for a resource. Please click on "More Options" to display these URLs

Report: Addition of an URL to that resource

Please note: Sometimes more than one URL is available for a resource. Please click on "More Options" to display these URLs

Report: The language(s) of that resource is/are…

Please note: Required language not in the list? Please use comment field

Report: Addition of an identifier to that resource

Report: report typo

Report: Add statement

Report: Add keywords

Please use the commentary box to describe other errors

Would you like to receive a feedback?

Commentary

Please type the code from the image

For general questions please use this address.

Persistent link: https://www.econbiz.de/10013054542