The Asymptotic Variance Of Semiparametric Estimotors
This paper derives a general formula for the asymptotic variance of semiparametric estimators that accounts for the presence of nonparametric estimators of functions. The general formula is specialized to show invariance of the asymptotic variance to the type of nonparametric estimator and to obtain correction terms for estimation of densities and mean-square projections (including conditional expectations). Regularity conditions for the validity of the formula are also given, including primitive conditions for asymptotic normality when series estimators are present. New examples considered include a semiparametric panel probit estimator and a series estimator of the average derivative. Copyright 1994 by The Econometric Society.
(This abstract was borrowed from another version of this item.)
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:||1989|
|Contact details of provider:|| Postal: (609) 258-4000|
Phone: (609) 258-4000
Fax: (609) 258-6419
Web page: http://www.princeton.edu/~erp/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:fth:prinem:346. 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: (Thomas Krichel)
If references are entirely missing, you can add them using this form.