قارن الطرق

	نموذج الانحدار الذاتي الانتقالي السلس (STAR)	نموذج ARFIMA: نموذج الانحدار الذاتي والمتوسط المتحرك المدمج كسريًا	انحدار الكوانتيل
المجال	الاقتصاد القياسي	الاقتصاد القياسي	الاقتصاد القياسي
العائلة	Regression model	Regression model	Regression model
سنة النشأة	1994	1980	1978
صاحب الطريقة	Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)	Granger & Joyeux (1980); Hosking (1981)	Koenker & Bassett
النوع	Nonlinear time-series regime-switching model	Long-memory time series model	Conditional quantile regression
المصدر التأسيسي	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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
الأسماء البديلة	smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR	fractionally integrated ARMA, long-memory time series model, ARFIMA / FIGARCH, fractional differencing model	conditional quantile regression, regression quantiles, Kantil Regresyon
ذات صلة	4	5	5
الملخص	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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.