Abstract
This paper studies higher-order inference properties of nonparametric local polynomial regression methods under random sampling. We prove Edgeworth expansions for t statistics and coverage error expansions for interval estimators that (i) hold uniformly in the data generating process, (ii) allow for the uniform kernel, and (iii) cover estimation of derivatives of the regression function. The terms of the higher-order expansions, and their associated rates as a function of the sample size and bandwidth sequence, depend on the smoothness of the population regression function, the smoothness exploited by the inference procedure, and on whether the evaluation point is in the interior or on the boundary of the support. We prove that robust bias corrected confidence intervals have the fastest coverage error decay rates in all cases, and we use our results to deliver novel, inference-optimal bandwidth selectors. The main methodological results are implemented in companion and software packages.
Funding Statement
The second author gratefully acknowledges financial support from the National Science Foundation (SES 1357561, SES 1459931, and SES-1947805) and the National Institutes of Health (R01 GM072611-16). The third author gratefully acknowledges financial support from the Richard N. Rosett and John E. Jeuck Fellowships.
Acknowledgements
We especially thank an Associate Editor, and the reviewers, for insightful comments that improve our manuscript. We also thank Chris Hansen, Michael Jansson, Adam McCloskey, Rocio Titiunik, and participants at various seminars and conferences for comments.
Citation
Sebastian Calonico. Matias D. Cattaneo. Max H. Farrell. "Coverage error optimal confidence intervals for local polynomial regression." Bernoulli 28 (4) 2998 - 3022, November 2022. https://doi.org/10.3150/21-BEJ1445
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