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Inference after Estimation of Breaks

Author

Listed:
  • Isaiah Andrews

    (Institute for Fiscal Studies and Harvard University)

  • Toru Kitagawa

    (Institute for Fiscal Studies and University College London)

  • Adam McCloskey

    (Institute for Fiscal Studies and Brown University)

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 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.

Suggested Citation

  • Isaiah Andrews & Toru Kitagawa & Adam McCloskey, 2020. "Inference after Estimation of Breaks," CeMMAP working papers CWP34/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:34/20
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    Cited by:

    1. Isaiah Andrews & Toru Kitagawa & Adam McCloskey, 2024. "Inference on Winners," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(1), pages 305-358.
    2. Christis Katsouris, 2023. "Break-Point Date Estimation for Nonstationary Autoregressive and Predictive Regression Models," Papers 2308.13915, arXiv.org.
    3. Ryo Okui, 2024. "The 2023 Japanese Economic Association Nakahara Prize: Recipient—Prof. Toru Kitagawa, Brown University and University College London," The Japanese Economic Review, Springer, vol. 75(3), pages 405-406, July.
    4. Oguzhan Akgun & Ryo Okui, 2025. "Robust Inference Methods for Latent Group Panel Models under Possible Group Non-Separation," Papers 2511.18550, arXiv.org.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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