Instrumental Variables Regression with Weak Instruments / Douglas Staiger, James H. Stock.

Format:

Book

Author/Creator:

Staiger, Douglas.

Contributor:

National Bureau of Economic Research.

Stock, James H.

Series:

Technical Working Paper Series (National Bureau of Economic Research) no. t0151.

NBER technical working paper series no. t0151

Language:

English

Subjects (All):

Economic history.

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 1994.

Cambridge, Massachusetts : National Bureau of Economic Research, 1994.

Summary:

This paper develops asymptotic distribution theory for instrumental variable regression when the partial correlation between the instruments and a single included endogenous variable is weak, here modeled as local to zero. Asymptotic representations are provided for various instrumental variable statistics, including the two-stage least squares (TSLS) and limited information maximum- likelihood (LIML) estimators and their t-statistics. The asymptotic distributions are found to provide good approximations to sampling distributions with just 20 observations per instrument. Even in large samples, TSLS can be badly biased, but LIML is, in many cases, approximately median unbiased. The theory suggests concrete quantitative guidelines for applied work. These guidelines help to interpret Angrist and Krueger's (1991) estimates of the returns to education: whereas TSLS estimates with many instruments approach the OLS estimate of 6%, the more reliable LIML and TSLS estimates with fewer instruments fall between 8% and 10%, with a typical confidence interval of (6%, 14%).

Notes:

Print version record

January 1994.