Heteroscedasticity Resistant Robust Covariance Matrix Estimator
It is straightforward that breaking the orthogonality condition implies biased and inconsistent estimates by means of the ordinary least squares . If moreover, the data are contaminatedit may significantly worsen the data processing, even if it is performed by instrumental variables or the (scaled) total least squares . That is why the method of instrumental weighted variables based of weighting down order statistics of squared residuals (rather than directly squared residuals) was proposed. The main underlying idea of this method is recalled and discussed. Then it is also recalled that neglecting heteroscedasticity may end up in significantly wrong specification and identification of regression model, just due to wrong evaluation of significance of the explanatory variables . So, if the test of heteroscedasticity (which is in the case when we use the instrumental weighted variables just robustified version of the classical White test for heteroscedasticity) rejects the hypothesis of homoscedasticity, we need an estimator of covariance matrix (of the estimators of regression coefficients) resistant to heteroscedasticity . The proposal of such an estimator is the main result of the paper. At the end of paper the numerical study of the proposed estimator (together with results offering comparison of model estimation by means of the ordinary least squares, the least weighted squares and by the instrumental weighted variables) is included.
Volume (Year): 17 (2010)
Issue (Month): 27 ()
|Contact details of provider:|| Web page: http://ces.utia.cas.cz|
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:czx:journl:v:17:y:2010:i:27:id:176. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Jozef Barunik)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.