Bootstrap tests for overidentification in linear regression models

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 3 ; Year: 2015 ; Issue: 4 ; Pages: 825-863 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft ; Econometric and Statistical Methods and Methodology: General ; Hypothesis Testing: General ; Statistical Simulation Methods: General ; Multiple or Simultaneous Equation Models; Multiple Variables: General
• Subject: Sargan test ; Basmann test ; Anderson-Rubin test ; weak instruments

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Davidson, Russell ; MacKinnon, James G.
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2015

Further information

• DOI: doi:10.3390/econometrics3040825
• Handle: http://hdl.handle.net/10419/171850
• Last update: 10.03.2025, 11:42 AM CET