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Uniform Inference after Pretesting for Exogeneity with Heteroskedastic Data

Doko Tchatoka, Firmin and Wang, Wenjie (2021): Uniform Inference after Pretesting for Exogeneity with Heteroskedastic Data.

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Abstract

Pretesting for exogeneity has become a routine in many empirical applications involving instrumental variables (IVs) to decide whether the ordinary least squares (OLS) or the two-stage least squares (2SLS) method is appropriate. Guggenberger (2010) shows that the second-stage t-test – based on the outcome of a Durbin-Wu-Hausman type pretest for exogeneity in the first-stage – has extreme size distortion with asymptotic size equal to 1 when the standard asymptotic critical values are used. In this paper, we first show that the standard wild bootstrap procedures (with either independent or dependent draws of disturbances) are not viable solutions to such extreme size-distortion problem. Then, we propose a novel hybrid bootstrap approach, which combines the wild bootstrap along with an adjusted Bonferroni size-correction method. We establish uniform validity of this hybrid bootstrap under conditional heteroskedasticity in the sense that it yields a two-stage test with correct asymptotic size. Monte Carlo simulations confirm our theoretical findings. In particular, our proposed hybrid method achieves remarkable power gains over the 2SLS-based t-test, especially when IVs are not very strong.

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