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Unbiased Instrumental Variables Estimation Under Known First-Stage Sign, Isaiah Andrews, Timothy B. Armstrong
Unbiased Instrumental Variables Estimation Under Known First-Stage Sign, Isaiah Andrews, Timothy B. Armstrong
Cowles Foundation Discussion Papers
We derive mean-unbiased estimators for the structural parameter in instrumental variables models 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 while retaining unbiasedness in finite samples. We show numerically that unbiasedness does not come at a cost of increased dispersion: in the single instrument case, the unbiased estimator is less dispersed than the 2SLS estimator. Our finite-sample …
Unbiased Instrumental Variables Estimation Under Known First-Stage Sign, Isaiah Andrews, Timothy B. Armstrong
Unbiased Instrumental Variables Estimation Under Known First-Stage Sign, Isaiah Andrews, Timothy B. Armstrong
Cowles Foundation Discussion Papers
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 …
Unbiased Instrumental Variables Estimation Under Known First-Stage Sign, Isaiah Andrews, Timothy B. Armstrong
Unbiased Instrumental Variables Estimation Under Known First-Stage Sign, Isaiah Andrews, Timothy B. Armstrong
Cowles Foundation Discussion Papers
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 …
Unbiased Instrumental Variables Estimation Under Known First-Stage Sign, Isaiah Andrews, Timothy B. Armstrong
Unbiased Instrumental Variables Estimation Under Known First-Stage Sign, Isaiah Andrews, Timothy B. Armstrong
Cowles Foundation Discussion Papers
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 non-randomized 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 …
Unbiased Instrumental Variables Estimation Under Known First-Stage Sign, Isaiah Andrews, Timothy B. Armstrong
Unbiased Instrumental Variables Estimation Under Known First-Stage Sign, Isaiah Andrews, Timothy B. Armstrong
Cowles Foundation Discussion Papers
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 …
Unbiased Instrumental Variables Estimation Under Known First-Stage Sign, Isaiah Andrews, Timothy B. Armstrong
Unbiased Instrumental Variables Estimation Under Known First-Stage Sign, Isaiah Andrews, Timothy B. Armstrong
Cowles Foundation Discussion Papers
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 non-randomized 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 …