Bootstrap Tests for Overidentification in Linear Regression Models
AbstractWe study the finite-sample properties of tests for overidentifying restrictions in linear regression models with a single endogenous regressor and weak instruments. Under the assumption of Gaussian disturbances, we derive expressions for a variety of test statistics as functions of eight mutually independent random variables and two nuisance parameters. The distributions of the statistics are shown to have an ill-defined limit as the parameter that determines the strength of the instruments tends to zero and as the correlation between the disturbances of the structural and reduced-form equations tends to plus or minus one. This makes it impossible to perform reliable inference near the point at which the limit is ill-defined. Several bootstrap procedures are proposed. They alleviate the problem and allow reliable inference when the instruments are not too weak. We also study their power properties. View Full-Text
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Davidson, R.; MacKinnon, J.G. Bootstrap Tests for Overidentification in Linear Regression Models. Econometrics 2015, 3, 825-863.
Davidson R, MacKinnon JG. Bootstrap Tests for Overidentification in Linear Regression Models. Econometrics. 2015; 3(4):825-863.Chicago/Turabian Style
Davidson, Russell; MacKinnon, James G. 2015. "Bootstrap Tests for Overidentification in Linear Regression Models." Econometrics 3, no. 4: 825-863.