{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/factor-driven-two-regime-regression","title":"Factor-Driven Two-Regime Regression","arxiv_id":"1810.11109","date":"2020-09-10","proceeding":null,"authors":[],"abstract":"We propose a novel two-regime regression model where regime switching is\ndriven by a vector of possibly unobservable factors. When the factors are\nlatent, we estimate them by the principal component analysis of a panel data\nset. We show that the optimization problem can be reformulated as mixed integer\noptimization, and we present two alternative computational algorithms. We\nderive the asymptotic distribution of the resulting estimator under the scheme\nthat the threshold effect shrinks to zero. In particular, we establish a phase\ntransition that describes the effect of first-stage factor estimation as the\ncross-sectional dimension of panel data increases relative to the time-series\ndimension. Moreover, we develop bootstrap inference and illustrate our methods\nvia numerical studies.","url_abs":"http://arxiv.org/abs/1810.11109v4","url_pdf":"http://arxiv.org/pdf/1810.11109v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"factor-driven-two-regime-regression","repo_url":"https://github.com/yshin12/fadtwo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"two","task_name":"Vocal Bursts Valence Prediction"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}