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Wild bootstrap tests for IV regression. (English) Zbl 1198.62035

Summary: We propose a wild bootstrap procedure for linear regression models estimated by instrumental variables. Like other bootstrap procedures that we proposed elsewhere, it uses efficient estimates of the reduced-form equation\((s)\). Unlike earlier procedures, it takes account of possible heteroscedasticity of unknown form. We apply this procedure to \(t\) tests, including heteroscedasticity-robust \(t\) tests, and to the T. W. Anderson and H. Rubin [Ann. Math. Stat. 20, 46–63 (1949; Zbl 0033.08002)] 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.

MSC:

62G09 Nonparametric statistical resampling methods
62J05 Linear regression; mixed models
65C60 Computational problems in statistics (MSC2010)
62G15 Nonparametric tolerance and confidence regions
62G10 Nonparametric hypothesis testing

Citations:

Zbl 0033.08002
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