Сравнение на методи

	TAR / SETAR: Авторегресия с праг за времеви редове с превключване на режими	Модел на авторегресия с плавен преход (STAR)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1990	1994
Създател	Howell Tong	Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)
Тип	Nonlinear time-series model with regime switching	Nonlinear time-series regime-switching model
Основополагащ източник	Tong, H. (1990). Non-linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 978-0-19-852300-6	Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218.
Други названия	Threshold Autoregression, Self-Exciting Threshold Autoregression, SETAR Model, Eşik Otoregresyon	smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR
Свързани	2	4
Резюме	TAR and SETAR are nonlinear autoregressive models introduced by Howell Tong (1990) that allow a time series to follow different linear dynamics in distinct regimes, separated by one or more threshold values. SETAR is the self-exciting variant, in which the threshold variable is a lagged value of the series itself, making it particularly suited to cycles, asymmetric adjustment, and limit-cycle behavior observed in economic and financial data.	The Smooth Transition Autoregressive (STAR) model is a nonlinear time-series model, developed in Teräsvirta's 1994 framework, that lets the dynamics move smoothly rather than abruptly between two regimes. The logistic variant (LSTAR) captures asymmetric business cycles and the exponential variant (ESTAR) captures purchasing-power-parity deviations.