Applications of James–Stein Shrinkage (II): Bias Reduction in Instrumental Variable Estimation
نویسنده
چکیده
In a two-stage linear regression model with Normal noise, I consider James–Stein type shrinkage in the estimation of the first-stage instrumental variable coefficients. For at least four instrumental variables and a single endogenous regressor, I show that the standard two-stage least-squares estimator is dominated with respect to bias. I construct the dominating estimator by a variant of James–Stein shrinkage in a first-stage high-dimensional Normal-means problem followed by a control-function approach in the second stage; it preserves invariances of the structural instrumental variable equations.
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