Testing Overidentifying Restrictions with Many Instruments and Heteroskedasticity Using Regularized Jackknife IV

Econometrics Journal (2021), volume 00, pp. 1–27

28 Pages Posted: 3 Nov 2021 Last revised: 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 29, 2021

Abstract

This paper proposes a new overidentifying restrictions test in a linear model when the number of instruments (possibly weak) may be smaller or larger than the sample size n or even infinite in a heteroskedastic framework. The proposed J test combines two techniques: the Jackknife method and the regularization technique which consists in stabilizing the projection matrix. We theoretically show that our new test achieves the asymptotically correct size in the presence of many instruments. The simulation results demonstrate that our modified J statistic test has better empirical properties in small samples than existing J tests. We also propose a regularized F-test to assess the strength of the instruments, which is robust to heteroskedasticity and many instruments.

Keywords: Overidenti cation tests, Many instruments, Weak instruments, Heteroskedasticity, Regularization method

JEL Classification: C12, C26, C55

Suggested Citation

Carrasco, Marine and Doukali, Mohamed, Testing Overidentifying Restrictions with Many Instruments and Heteroskedasticity Using Regularized Jackknife IV (June 29, 2021). Econometrics Journal (2021), volume 00, pp. 1–27, Available at SSRN: https://ssrn.com/abstract=3955372

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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