EconPapers    
Economics at your fingertips  
 

A Risk Superior Semiparametric Estimator for Overidentified Linear Models

George G. Judge and Ron C. Mittelhammer

A chapter in 30th Anniversary Edition, 2012, pp 237-255 from Emerald Group Publishing Limited

Abstract: In the context of competing IV econometric models and estimators, we demonstrate a semiparametric Stein-like estimator (SSLE) that, under quadratic loss, has superior risk performance. The method eliminates the need for pretesting to decide whether covariate endogeneity is present and makes use of a pretest estimator choice between IV and non-IV methods unnecessary. A sampling study is used to illustrate finite sample performance over a range of sampling designs, including its performance relative to pretest estimators. An important applied problem from the literature is analyzed to indicate possible applied implications and the relation of SSLE to other modern IV estimators.

Keywords: Combining estimators; semiparametric estimation and inference; quadratic loss; temparal structure (search for similar items in EconPapers)
Date: 2012
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... 9053(2012)0000030013
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-9053(2012)0000030013

DOI: 10.1108/S0731-9053(2012)0000030013

Access Statistics for this chapter

More chapters in Advances in Econometrics from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().

 
Page updated 2025-04-15
Handle: RePEc:eme:aecozz:s0731-9053(2012)0000030013