Improved Jive estimators for overidentified linear models with and without heteroskedasticity

Files in This Item:
File Description SizeFormat 
devereuxp_workpap_011.pdf165.84 kBAdobe PDFDownload
Title: Improved Jive estimators for overidentified linear models with and without heteroskedasticity
Authors: Devereux, Paul J.
Ackerberg, Daniel A.
Permanent link: http://hdl.handle.net/10197/749
Date: Aug-2008
Abstract: We introduce two simple new variants of the Jackknife Instrumental Variables (JIVE) estimator for overidentified linear models and show that they are superior to the existing JIVE estimator, significantly improving on its small sample bias properties. We also compare our new estimators to existing Nagar (1959) type estimators. We show that, in models with heteroskedasticity, our estimators have superior properties to both the Nagar estimator and the related B2SLS estimator suggested in Donald and Newey (2001). These theoretical results are verified in a set of Monte-Carlo experiments and then applied to estimating the returns to schooling using actual data.
Type of material: Working Paper
Publisher: University College Dublin. School of Economics
Copyright (published version): UCD School of Economics 2008
Subject LCSH: Jackknife (Statistics)
Linear models (Statistics)
Human capital--Mathematical models
Language: en
Status of Item: Not peer reviewed
Appears in Collections:Economics Working Papers & Policy Papers

Show full item record

Google ScholarTM

Check


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.