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Inference after estimation of breaks

Andrews, I; Kitagawa, T; McCloskey, A; (2020) Inference after estimation of breaks. Journal of Econometrics 10.1016/j.jeconom.2020.07.036. (In press).

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Abstract

In an important class of econometric problems, researchers select a target parameter by maximizing the Euclidean norm of a data-dependent vector. Examples that can be cast into this frame include threshold regression models with estimated thresholds and structural break models with estimated break dates. Estimation and inference procedures that ignore the randomness of the target parameter can be severely biased and misleading when this randomness is non-negligible. This paper studies conditional and unconditional inference in such settings, accounting for the data-dependent choice of target parameters. We detail the construction of quantile-unbiased estimators and confidence sets with correct coverage, and prove their asymptotic validity under data generating process such that the target parameter remains random in the limit. We also provide a novel sample splitting approach that improves on conventional split-sample inference.

Type: Article
Title: Inference after estimation of breaks
DOI: 10.1016/j.jeconom.2020.07.036
Publisher version: https://doi.org/10.1016/j.jeconom.2020.07.036
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Selective inference, Sample splitting, Structural breaks, Threshold regression, Misspecification
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/10105441
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