Weighted samples, kernel density estimators and convergence
This note extends the standard kernel density estimator to the case of weighted samples in several ways. In the first place I consider the obvious extension by substituting the simple sum in the definition of the estimator by a weighted sum, but I also consider other alternatives of introducing weights, based on adaptive kernel density estimators, and consider the weights as indicators of the informational content of the observations and in this sense as signals of the local density of the data. All these ideas are shown using the Penn World Table in the context of the macroeconomic convergence issue. Copyright Springer-Verlag Berlin Heidelberg 2003
Volume (Year): 28 (2003)
Issue (Month): 2 (04)
|Contact details of provider:|| Postal: |
Phone: ++43 - (0)1 - 599 91 - 0
Fax: ++43 - (0)1 - 599 91 - 555
Web page: http://link.springer.de/link/service/journals/00181/index.htm
More information through EDIRC
|Order Information:||Web: http://link.springer.de/orders.htm|
When requesting a correction, please mention this item's handle: RePEc:spr:empeco:v:28:y:2003:i:2:p:335-351. 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: (Guenther Eichhorn)or (Christopher F Baum)
If references are entirely missing, you can add them using this form.