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Bootstrap Tests for Overidentification in Linear Regression Models

Abstract : We 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.
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https://hal-amu.archives-ouvertes.fr/hal-01456100
Contributor : Patrice Cacciuttolo <>
Submitted on : Friday, February 3, 2017 - 11:58:08 PM
Last modification on : Wednesday, August 5, 2020 - 3:13:00 AM

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  • HAL Id : hal-01456100, version 1

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Russell Davidson, James G. Mackinnon. Bootstrap Tests for Overidentification in Linear Regression Models. Econometrics, 2015, 3 (4), pp.825--863. ⟨hal-01456100⟩

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