Σύγκριση μεθόδων

	Μοντέλο Ομαλής Μετάβασης Αυτοπαλίνδρομης Συσχέτισης (STAR)	ARFIMA Model
Πεδίο	Οικονομετρία	Οικονομετρία
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	1994	1980
Δημιουργός	Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)	Granger & Joyeux (1980); Hosking (1981)
Τύπος	Nonlinear time-series regime-switching model	Long-memory time series model
Θεμελιώδης πηγή	Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218.	Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15–29.
Εναλλακτικές ονομασίες	smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR	fractionally integrated ARMA, long-memory time series model, ARFIMA / FIGARCH, fractional differencing model
Συναφείς	4	5
Σύνοψη	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.	ARFIMA is a time series model that captures long-memory behaviour using a fractional differencing parameter d, generalising the integer differencing of ARIMA. It was introduced by Granger and Joyeux (1980) and formalised by Hosking (1981) to describe series whose autocorrelations decay slowly rather than abruptly.