Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/189411 
Year of Publication: 
2008
Series/Report no.: 
Queen's Economics Department Working Paper No. 1135
Publisher: 
Queen's University, Department of Economics, Kingston (Ontario)
Abstract: 
We propose a wild bootstrap procedure for linear regression models estimated by instrumental variables. Like other bootstrap procedures that we have proposed elsewhere, it uses efficient estimates of the reduced-form equation(s). Unlike them, it takes account of possible heteroskedasticity of unknown form. We apply this procedure to t tests, including heteroskedasticity-robust t tests, and to the Anderson-Rubin test. We provide simulation evidence that it works far better than older methods, such as the pairs bootstrap. We also show how to obtain reliable confidence intervals by inverting bootstrap tests. An empirical example illustrates the utility of these procedures.
Subjects: 
Instrumental variables
two-stage least squares
wild bootstrap
pairs bootstrap
residual bootstrap
weak instruments
confidence intervals
JEL: 
C12
C15
C30
Document Type: 
Working Paper

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