{"ID":2855619,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.12262","arxiv_id":"2510.12262","title":"Optimal break tests for large linear time series models","abstract":"We develop a class of optimal tests for a structural break occurring at an unknown date in infinite and growing-order time series regression models, such as AR($\\infty$), linear regression with increasingly many covariates, and nonparametric regression. Under an auxiliary i.i.d. Gaussian error assumption, we derive an average power optimal test, establishing a growing-dimensional analog of the exponential tests of Andrews and Ploberger (1994) to handle identification failure under the null hypothesis of no break. Relaxing the i.i.d. Gaussian assumption to a more general dependence structure, we establish a functional central limit theorem for the underlying stochastic processes, which features an extra high-order serial dependence term due to the growing dimension. We robustify our test both against this term and finite sample bias and illustrate its excellent performance and practical relevance in a Monte Carlo study and a real data empirical example.","short_abstract":"We develop a class of optimal tests for a structural break occurring at an unknown date in infinite and growing-order time series regression models, such as AR($\\infty$), linear regression with increasingly many covariates, and nonparametric regression. Under an auxiliary i.i.d. Gaussian error assumption, we derive an...","url_abs":"https://arxiv.org/abs/2510.12262","url_pdf":"https://arxiv.org/pdf/2510.12262v1","authors":"[\"Abhimanyu Gupta\",\"Myung Hwan Seo\"]","published":"2025-10-14T08:13:23Z","proceeding":"econ.EM","tasks":"[\"econ.EM\",\"math.ST\"]","methods":"[]","has_code":false}