WEAK-IDENTIFICATION ROBUST WILD BOOTSTRAP APPLIED TO A CONSISTENT MODEL SPECIFICATION TEST
Jonathan B. Hill · Econometric Theory · 2020
We present a new robust bootstrap method for a test when there is a nuisance parameter under the alternative, and some parameters are possibly weakly or nonidentified. We focus on a Bierens (1990,Econometrica58, 1443–1458)-type conditional moment test of omitted nonlinearity for convenience. Existing methods include the supremump-value which promotes a conservative test that is generally not consistent, and test statistic transforms like the supremum and average for which bootstrap methods are not valid under weak identification. We propose a new wild bootstrap method forp-value computation by targeting specific identification cases. We then combine bootstrappedp-values across polar identification cases to form an asymptotically validp-value approximation that is robust to any identification case. Our wild bootstrap procedure does not require knowledge of the covariance structure of the bootstrapped processes, whereas Andrews and Cheng’s (2012a,Econometrica80, 2153–2211; 2013,Journal of Econometrics173, 36–56; 2014,Econometric Theory30, 287–333) simulation approach generally does. Our method allows for robust bootstrap critical value computation as well. Our bootstrap method (like conventional ones) does not lead to a consistentp-value approximation for test statistic functions like the supremum and average. Therefore, we smooth over the robust bootstrappedp-value as the basis for several tests which achieve the correct asymptotic level, and are consistent, for any degree of identification. They also achieve uniform size control. A simulation study reveals possibly large empirical size distortions in nonrobust tests when weak or nonidentification arises. One of our smoothedp-value tests, however, dominates all other tests by delivering accurate empirical size and comparatively high power.