Inference after estimation of breaks

Isaiah Andrews (), Toru Kitagawa () and Adam McCloskey
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Isaiah Andrews: Institute for Fiscal Studies and Harvard University
Toru Kitagawa: Institute for Fiscal Studies and University College London

No CWP51/19, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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 breakdates. 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 proposes conditional and unconditional inference in such settings, reflecting 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.

Date: 2019-10-15
New Economics Papers: this item is included in nep-ecm
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Related works:
Journal Article: Inference after estimation of breaks (2021)
Working Paper: Inference after Estimation of Breaks (2020)
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