On B-Robust Instrumental Variable Estimation of the LinearModel
The aim of this paper is to demonstrate how to obtain robust (with respect to outlying observations) consistent estimates of the linear model when the fundamental orthogonality condition is not fulfilled. With this end in view, we develop two estimation procedures: Two Stage Generalized M (2SGM) and Robust Generalized Method of Moments (RGMM). Both estimators are consistent, asymptotically normally distributed, and B-robust, i.e. their associated influence function is bounded. Our simulation results indicate that the relatively efficient RGMM estimator (in regressions with heteroskedastic and/or autocorrelated errors) provides accurate parameter esrtimates of a panel data model whose explanatory factors are subject to measurement errors, even if a substantial portion of the data is contaminated with aberrant observations. The traditional estimation techniques such as 2SLS and GMM break down when outliers corrupt the data.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://www.igier.unibocconi.it/
|Order Information:|| Web: http://www.igier.unibocconi.it/en/papers/index.htm Email: |
When requesting a correction, please mention this item's handle: RePEc:igi:igierp:131. 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: ()
If references are entirely missing, you can add them using this form.