Salīdzināt metodes

	TAR / SETAR: Robuste autoregresija režīmu pārslēgšanās laika sērijām	Mūsdienu pārejas autoregresijas (STAR) modelis	Regresija ar slieksni
Nozare	Ekonometrija	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model	Regression model
Izcelsmes gads	1990	1994	2000
Autors	Howell Tong	Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)	Bruce E. Hansen
Tips	Nonlinear time-series model with regime switching	Nonlinear time-series regime-switching model	Nonlinear regime-switching regression
Pirmavots	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.	Hansen, B. E. (2000). Sample Splitting and Threshold Estimation. Econometrica, 68(3), 575-603.
Citi nosaukumi	Threshold Autoregression, Self-Exciting Threshold Autoregression, SETAR Model, Eşik Otoregresyon	smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR	threshold model, regime-switching regression, sample splitting model, Eşik Değer Regresyonu (Threshold Regression)
Saistītās	2	4	5
Kopsavilkums	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.	Threshold regression is a nonlinear, regime-switching model in which the regression parameters take different values above and below an estimated threshold value of a threshold variable. The sample-splitting and threshold-estimation framework was developed by Bruce E. Hansen (2000) and is widely used for time-series and panel data with structural breaks and regime-dependent relationships.