Robust Semiparametric Estimation in the Presence of Heterogeneity of Unknown Form
We show that semiparametric adaptive maximum likelihood estimators have desirable robustness properties when the innivations in a location parameter model are uncorrelated but not necessarily independent. We show that such estimators have asymptotic covariance matrices equal to the inverse of the Fisher information of the unconditional distribution of the data in the presence of general forms of heterogeneity, including conditional dependence in even moments.
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:||1996|
|Date of revision:|
|Contact details of provider:|| Postal: University of Rochester, Center for Economic Research, Department of Economics, Harkness 231 Rochester, New York 14627 U.S.A.|
When requesting a correction, please mention this item's handle: RePEc:roc:rocher:416. 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: (Richard DiSalvo)
If references are entirely missing, you can add them using this form.