Instrument Relevance in Multivariate Linear Models: A Simple Measure
20 Pages Posted: 15 Sep 2000 Last revised: 13 Jul 2024
Date Written: March 1996
Abstract
The correlation between instruments and explanatory variables is a key determinant of the performance of the instrumental variables estimator. The R-squared from regressing the explanatory variable on the instrument vector is a useful measure of relevance in univariate models, but can be misleading when there are multiple endogenous variables. This paper proposes a computationally simple partial R- squared measure of instrument relevance for multivariate models.
Suggested Citation: Suggested Citation
Do you have a job opening that you would like to promote on SSRN?
Recommended Papers
-
Instrumental Variables Regression with Weak Instruments
By Douglas Staiger and James H. Stock
-
Testing for Weak Instruments in Linear IV Regression
By James H. Stock and Motohiro Yogo
-
Testing for Weak Instruments in Linear IV Regression
By James H. Stock and Motohiro Yogo
-
A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments
By James H. Stock, Jonathan H. Wright, ...
-
By Charles R. Nelson and Richard Startz
-
Some Further Results on the Exact Small Sample Properties of the Instrumental Variable Estimator
By Charles R. Nelson and Richard Startz
-
A New Specification Test for the Validity of Instrumental Variables
By Jinyong Hahn and Jerry A. Hausman
-
Consistent Estimation with a Large Number of Weak Instruments
By John C. Chao and Norman R. Swanson
-
Does Head Start Make a Difference?
By Janet Currie and Duncan Thomas
-
Asymptotic Distributions of Instrumental Variables Statistics with Many Instruments
By James H. Stock and Motohiro Yogo