Artikel
Unbiased instrumental variables estimation under known first-stage sign
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.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Quantitative Economics ; ISSN: 1759-7331 ; Volume: 8 ; Year: 2017 ; Issue: 2 ; Pages: 479-503 ; New Haven, CT: The Econometric Society
- Klassifikation
-
Wirtschaft
Estimation: General
Single Equation Models: Single Variables: Instrumental Variables (IV) Estimation
- Thema
-
Unbiased estimation
weak instruments
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Andrews, Isaiah
Armstrong, Timothy B.
- Ereignis
-
Veröffentlichung
- (wer)
-
The Econometric Society
- (wo)
-
New Haven, CT
- (wann)
-
2017
- DOI
-
doi:10.3982/QE700
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
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Objekttyp
- Artikel
Beteiligte
- Andrews, Isaiah
- Armstrong, Timothy B.
- The Econometric Society
Entstanden
- 2017
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