Artikel

Simple and honest confidence intervals in nonparametric regression

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.

Sprache
Englisch

Erschienen in
Journal: Quantitative Economics ; ISSN: 1759-7331 ; Volume: 11 ; Year: 2020 ; Issue: 1 ; Pages: 1-39 ; New Haven, CT: The Econometric Society

Klassifikation
Wirtschaft
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Thema
Confidence intervals
regression discontinuity
nonparametric regression

Ereignis
Geistige Schöpfung
(wer)
Armstrong, Timothy B.
Kolesár, Michal
Ereignis
Veröffentlichung
(wer)
The Econometric Society
(wo)
New Haven, CT
(wann)
2020

DOI
doi:10.3982/QE1199
Handle
Letzte Aktualisierung
10.03.2025, 11:41 MEZ

Datenpartner

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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Armstrong, Timothy B.
  • Kolesár, Michal
  • The Econometric Society

Entstanden

  • 2020

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