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• W3125326898 abstract "We consider the problem of constructing honest confidence intervals (CIs) for a scalar parameter of interest, such as the regression discontinuity parameter, in nonparametric regression based on kernel or local polynomial estimators. To ensure that our CIs are honest, we use critical values that take into account the possible bias of the estimator upon which the CIs are based. We show that this approach leads to CIs that are more efficient than conventional CIs that achieve coverage by undersmoothing or subtracting an estimate of the bias. We give sharp efficiency bounds of using different kernels, and derive the optimal bandwidth for constructing honest CIs. We show that using the bandwidth that minimizes the maximum mean‐squared error results in CIs that are nearly efficient and that in this case, the critical value depends only on the rate of convergence. For the common case in which the rate of convergence is n −2/5 , the appropriate critical value for 95% CIs is 2.18, rather than the usual 1.96 critical value. We illustrate our results in a Monte Carlo analysis and an empirical application." @default.
• W3125326898 created "2021-02-01" @default.
• W3125326898 creator A5079301563 @default.
• W3125326898 creator A5085709680 @default.
• W3125326898 date "2020-01-01" @default.
• W3125326898 modified "2023-10-01" @default.
• W3125326898 title "Simple and honest confidence intervals in nonparametric regression" @default.
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• W3125326898 doi "https://doi.org/10.3982/qe1199" @default.
• W3125326898 hasPublicationYear "2020" @default.
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