A justification of conditional confidence intervals

E. Beutner, A. Heinemann, S. Smeekes

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

© 2021, Institute of Mathematical Statistics. All rights reserved.To quantify uncertainty around point estimates of conditional objects such as conditional means or variances, parameter uncertainty has to be taken into account. Attempts to incorporate parameter uncertainty are typically based on the unrealistic assumption of observing two independent processes, where one is used for parameter estimation, and the other for conditioning upon. Such unrealistic foundation raises the question whether these intervals are theoretically justified in a realistic setting. This paper presents an asymptotic justification for this type of intervals that does not require such an unrealistic assumption, but relies on a samplesplit approach instead. By showing that our sample-split intervals coincide asymptotically with the standard intervals, we provide a novel, and realistic, justification for confidence intervals of conditional objects. The analysis is carried out for a rich class of time series models. We also present the results of a simulation study to evaluate the performance of the sample-split approach. The results indicate that also in practice sample-split intervals might be more appropriate than the standard intervals.
Original languageEnglish
Pages (from-to)2517-2565
JournalElectronic Journal of Statistics
Volume15
Issue number1
DOIs
Publication statusPublished - 5 May 2021

Funding

The authors’ work was supported by the Netherlands Organization for Scientific Research (NWO), grant number: 406-15-020.

FundersFunder number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek406-15-020

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