Unbiased Instrumental Variables Estimation under Known First-Stage Sign
Isaiah Andrews and
Timothy B. Armstrong ()
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Isaiah Andrews: Harvard Society of Fellows
Timothy B. Armstrong: Cowles Foundation, Yale University, http://economics.yale.edu/people/timothy-b-armstrong
No 1984R2, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University
We derive mean-unbiased estimators for the structural parameter in instrumental variables models with a single endogenous regressor where the sign of one or more first stage coefficients is known. In the case with a single instrument, the unbiased estimator is unique. For cases with multiple instruments we propose a class of unbiased estimators and show that an estimator within this class is efficient when the instruments are strong. We show numerically that unbiasedness does not come at a cost of increased dispersion in models with a single instrument: in this case the unbiased estimator is less dispersed than the 2SLS estimator. Our finite-sample results apply to normal models with known variance for the reduced-form errors, and imply analogous results under weak instrument asymptotics with an unknown error distribution.
Keywords: Weak instruments; Unbiased estimation; Sign restrictions (search for similar items in EconPapers)
JEL-codes: C26 C36 (search for similar items in EconPapers)
Date: 2015-02, Revised 2015-09
Note: Includes Supplemental appendix
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