Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/195546 
Year of Publication: 
2017
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 8 [Issue:] 2 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2017 [Pages:] 479-503
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
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, there is a unique nonrandomized unbiased estimator based on the reduced-form and first-stage regression estimates. 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 two-stage least squares 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.
Subjects: 
Unbiased estimation
weak instruments
JEL: 
C13
C26
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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