Efficient estimation using regularized Jackknife IV estimator

Annals of Economics and Statistics No. 128 (December 2017), pp. 109-149.

47 Pages Posted: 18 Nov 2021

See all articles by Marine Carrasco

Marine Carrasco

University of Montreal - Departement de Ciences Economiques

Mohamed Doukali

McGill University, Department of Economics.

Date Written: June 01, 2017

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 semiparametric 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.

JEL Classification: C13, C26, C52

Suggested Citation

Carrasco, Marine and Doukali, Mohamed, Efficient estimation using regularized Jackknife IV estimator (June 01, 2017). Annals of Economics and Statistics No. 128 (December 2017), pp. 109-149., Available at SSRN: https://ssrn.com/abstract=3955086

Marine Carrasco

University of Montreal - Departement de Ciences Economiques ( email )

C.P. 6128, succursale Centre-Ville
Montreal, Quebec H3C 3J7
Canada
(514) 343-2394 (Phone)

HOME PAGE: http://www.sceco.umontreal.ca/liste_personnel/carrasco/index.htm

Mohamed Doukali (Contact Author)

McGill University, Department of Economics. ( email )

1001 Sherbrooke St. W
Montreal, Quebec H3A 1G5
Canada

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