On Optimal Inference in the Linear IV Model

113 Pages Posted: 2 Mar 2018

See all articles by Donald W. K. Andrews

Donald W. K. Andrews

Yale University - Cowles Foundation

Vadim Marmer

University of British Columbia (UBC) - Vancouver School of Economics

Zhengfei Yu

University of Tsukuba

Date Written: February 24, 2018

Abstract

This paper considers tests and confidence sets (CS’s) concerning the coefficient on the endogenous variable in the linear IV regression model with homoskedastic normal errors and one right-hand side endogenous variable. The paper derives a finite-sample lower bound function for the probability that a CS constructed using a two-sided invariant similar test has infinite length and shows numerically that the conditional likelihood ratio (CLR) CS of Moreira (2003) is not always “very close,” say .005 or less, to this lower bound function. This implies that the CLR test is not always very close to the two-sided asymptotically-efficient (AE) power envelope for invariant similar tests of Andrews, Moreira, and Stock (2006) (AMS).

On the other hand, the paper establishes the finite-sample optimality of the CLR test when the correlation between the structural and reduced-form errors, or between the two reduced-form errors, goes to 1 or -1 and other parameters are held constant, where optimality means achievement of the two-sided AE power envelope of AMS. These results cover the full range of (non-zero) IV strength.

The paper investigates in detail scenarios in which the CLR test is not on the two-sided AE power envelope of AMS. Also, theory and numerical results indicate that the CLR test is close to having greatest average power, where the average is over a grid of concentration parameter values and over pairs alternative hypothesis values of the parameter of interest, uniformly over pairs of alternative hypothesis values and uniformly over the correlation between the structural and reduced-form errors. Here, “close” means .015 or less for k≤20, where k denotes the number of IV’s, and .025 or less for 0 The paper concludes that, although the CLR test is not always very close to the two-sided AE power envelope of AMS, CLR tests and CS’s have very good overall properties.

Keywords: Conditional likelihood ratio test, Confidence interval, Infinite length, Linear instrumental variables, Optimal test, Weighted average power, Similar test

JEL Classification: C12, C36

Suggested Citation

Andrews, Donald W. K. and Marmer, Vadim and Yu, Zhengfei, On Optimal Inference in the Linear IV Model (February 24, 2018). Cowles Foundation Discussion Paper No. 2073R, Available at SSRN: https://ssrn.com/abstract=3132292 or http://dx.doi.org/10.2139/ssrn.3132292

Donald W. K. Andrews (Contact Author)

Yale University - Cowles Foundation ( email )

Box 208281
New Haven, CT 06520-8281
United States
203-432-3698 (Phone)
203-432-6167 (Fax)

Vadim Marmer

University of British Columbia (UBC) - Vancouver School of Economics ( email )

6000 Iona Dr
Vancouver, BC V6T 1L4
Canada

Zhengfei Yu

University of Tsukuba ( email )

Tsukuba University , Ibaraki Ken
Tsukuba, Ibaraki 305-8573, Ibaraki 3050006
Japan

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