Jackknife Instrumental Variables Estimation / Joshua D. Angrist, Guido W. Imbens, Alan Krueger.

Format:

Book

Author/Creator:

Angrist, Joshua D.

Contributor:

National Bureau of Economic Research.

Imbens, Guido W.

Krueger, Alan.

Series:

Technical Working Paper Series (National Bureau of Economic Research) no. t0172.

NBER technical working paper series no. t0172

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 1995.

Summary:

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.

Notes:

Print version record

February 1995.