centre for microdata methods and practice

ESRC centre

cemmap is an ESRC research centre


Keep in touch

Subscribe to cemmap news

Inference after Estimation of Breaks

Authors: Isaiah Andrews , Toru Kitagawa and Adam McCloskey
Date: 06 July 2020
Type: cemmap Working Paper, CWP34/20
DOI: 10.1920/wp.cem.2020.3420


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 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.

Download full version
Previous version:
Isaiah Andrews, Toru Kitagawa and Adam McCloskey October 2019, Inference after estimation of breaks, cemmap Working Paper, The IFS

Search cemmap

Search by title, topic or name.

Contact cemmap

Centre for Microdata Methods and Practice

How to find us

Tel: +44 (0)20 7291 4800

E-mail us