Bootstrapping an Identification Robust Max Correlation White Noise Test
This paper develops a bootstrapped white noise test for serial correlation that is based on the maximum correlation and is robust to weak identification in the parameters. The test is appropriate for residuals and requires only uncorrelatedness under the null hypothesis, rather than independence of the time series. We show that existing white noise tests can be extended to allow for bootstrapping models with known sources of identification failure via a modification of the first order expansion utilized by the dependent wild bootstrap. Basing our test on the most relevant sample serial correlation leads to high power, particularly against distant and weak serial dependence. with J.B. Hill.
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Testing Many Parameter Restrictions Under Weak Identification
Testing Many Zero Restrictions Where a Subset May Lie On the Boundary with J.B. Hill and Kaiji Motegi.