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Threshold Regression with a Nearly Integrated Threshold Variable — arXiv:2610.39871

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.39871
License (as archived): CC0-1.0
Archived: 2026-11-01T02:59:09.824Z
SHA-256 of the archived text: cff5fb2c5d4543bcef22e499828640f1765abd7c5c94ea682ce6cbe6300fa3ec
Stable URL of this page: https://metraipolis.com/documento/dc_681ea35c300a

How to cite

Threshold Regression with a Nearly Integrated Threshold Variable — arXiv:2610.39871 — arXiv (en), https://arxiv.org/abs/2610.39871 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_681ea35c300a (Metraipolis archive dc_681ea35c300a, sha256 cff5fb2c5d4543bcef22e499828640f1765abd7c5c94ea682ce6cbe6300fa3ec)

BibTeX

@misc{dc681ea35c300a,
  title = {Threshold Regression with a Nearly Integrated Threshold Variable — arXiv:2610.39871},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.39871},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_681ea35c300a; sha256 cff5fb2c5d4543bcef22e499828640f1765abd7c5c94ea682ce6cbe6300fa3ec},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.39871. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This paper considers threshold regression with an integrated or local-to-unity threshold variable and a threshold fixed in levels. We study the least squares threshold estimator and establish its asymptotic theory under both fixed and diminishing threshold effects. Under a fixed effect, the threshold estimator has a compound Cox argmin limit, whereas under a diminishing effect, its limiting distribution is characterized by a two-sided Brownian argmax with a random scale. We also develop likelihood ratio inference, which is asymptotically pivotal under a diminishing effect and conservative under Gaussian errors for a fixed effect, and show that conventional $t$-ratios for the slope coefficients remain asymptotically standard normal. A sup-Wald test is proposed to detect threshold effects that may be too small for consistent threshold localization. We further extend the framework to allow for integrated slope regressors and for contemporaneous endogeneity of the threshold variable, showing that the leading endogenous contribution cancels over shrinking threshold windows. Monte Carlo simulations examine the finite-sample performance of the proposed methods. An illustration using public debt and economic growth highlights the relevance of allowing for a persistent threshold variable.

Tema degli esploratori: economia dei servizi IA (elenco arXiv econ.EM, nuovi lavori del giorno).
Categorie arXiv: Econometrics (econ.EM).
Pagina del lavoro: https://arxiv.org/abs/2610.39871 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-01T02:24:58.934Z dalla pagina https://arxiv.org/list/econ.EM/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).