29 Pages Posted: 14 Jul 2000
Date Written: February 1995
Two-stage-least-squares (2SLS) estimates are biased towards OLS estimates. This bias grows with the degree of over-identification and can generate highly misleading results. In this paper we propose two simple alternatives to 2SLS and limited-information-maximum-likelihood (LIML) estimators for models with more instruments than endogenous regressors. These estimators can be interpreted as instrumental variables procedures using an instrument that is independent of disturbances even in finite samples. Independence is achieved by using a `leave-one-out' jackknife-type fitted value in place of the usual first-stage equation. The new estimators are first-order equivalent to 2SLS but with finite-sample properties superior to those of 2SLS and similar to LIML when there are many instruments. Moreover, the jackknife estimators appear to be less sensitive than LIML to deviations from the linear reduced form used in classical simultaneous equations models.
Suggested Citation: Suggested Citation
Angrist, Joshua D. and Imbens, Guido W. and Krueger, Alan B., Jackknife Instrumental Variables Estimation (February 1995). NBER Working Paper No. t0172. Available at SSRN: https://ssrn.com/abstract=225083
By John Shea