Efficient Estimation Using Regularized Jackknife IV Estimator

Marine Carrasco and Mohamed Doukali

Annals of Economics and Statistics, 2017, issue 128, 109-149

Abstract: We consider instrumental variables (IV) regression in a setting with many (possibly weak) instruments. In finite samples, the inclusion of an excessive number of moments may increase the bias of IV estimators. We propose a Jackknife instrumental variables estimator (RJIVE) combined with regularization techniques based on Tikhonov (T), Principal Components (PC) and Landweber Fridman (LF) methods to stabilize the projection matrix. We prove that the RJIVE is consistent and asymptotically normally distributed. Moreover, it reaches the semi parametric efficiency bound under certain conditions. We derive the rate of the approximate mean square error and propose a data-driven method for selecting the tuning parameter. Simulation results show that our proposed estimators provide more reliable confidence intervals than other regularized estimators.

Keywords: Many Instruments; Mean Square Error; Jackknife; Regularization Methods. (search for similar items in EconPapers)
JEL-codes: C13 C26 C52 (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.jstor.org/stable/10.15609/annaeconstat2009.128.0109 (text/html)

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:adr:anecst:y:2017:i:128:p:109-149

DOI: 10.15609/annaeconstat2009.128.0109

Access Statistics for this article

Annals of Economics and Statistics is currently edited by Laurent Linnemer

More articles in Annals of Economics and Statistics from GENES Contact information at EDIRC.
Bibliographic data for series maintained by Secretariat General () and Laurent Linnemer ().