first
prev
toc
Download Citation
Recommend
Mark
Alert
Link
Print
Computational and statistical efficiency of semiparametric gls estimators
Authors:
Joel L. Horowitz a;
George R. Neumann a
| Affiliation: | a Department of Economic, University of Iowa, Lowa, Lowa |
DOI:
10.1080/07474938908800172
Publication Frequency:
6 issues per year
Formats available:
PDF
(English)
Article Requests:
Order Reprints
:
Request Permissions
-
Single Article Purchase:
US$37.00
-
buy now
-
Sign In
- Online Sample
add to cart 
[ show other buying options ]
View Article:
View Article (PDF)
Abstract
In this note, we report a dramatic improvement in the computational efficiency of semiparametric generalized least squares(SGLS) estimation. Computation of SGLS estimates no longer presents serious problems with data sets of moderate size. We also correct a numerical error in the standard errors of the SGLS estimates reported in our recent paper in this journal (Horowitz and Neumann, 1987). The corrected standard errors of SGLS are comparable to those we reported for quantile estimates.
|
| Keywords: semiparametric estimation; distribution-free estimation; censored regression model; duration analysis |
| view references (3) |
|
Bookmark with:
|
CiteULike
Del.icio.us
BibSonomy
Connotea
More bookmarks
