A Justification of Conditional Confidence Intervals

Eric Beutner, Alexander Heinemann and Stephan Smeekes
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Eric Beutner: QE Math. Economics & Game Theory, RS: GSBE ETBC

No 23, Research Memorandum from Maastricht University, Graduate School of Business and Economics (GSBE)

Abstract: 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 sample-split 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 general class of Markov chains nesting various time series models.

JEL-codes: C22 C32 C53 G17 (search for similar items in EconPapers)
Date: 2017-10-10
New Economics Papers: this item is included in nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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https://cris.maastrichtuniversity.nl/ws/files/16594611/RM17023.pdf (application/pdf)

Related works:
Working Paper: A Justification of Conditional Confidence Intervals (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:unm:umagsb:2017023

DOI: 10.26481/umagsb.2017023

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