Coverage error optimal confidence intervals for local polynomial regression

Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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 R and Stata software packages.

Original languageEnglish (US)
Pages (from-to)2998-3022
Number of pages25
Issue number4
StatePublished - Nov 2022

All Science Journal Classification (ASJC) codes

  • Statistics and Probability


  • Cramér condition
  • Edgeworth expansion
  • bandwidth selection
  • minimax bound
  • nonparametric regression
  • optimal inference
  • robust bias correction


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