পদ্ধতির তুলনা করুন

	মসৃণ রূপান্তর স্বয়ংক্রীয় রিগ্রেশন (STAR) মডেল	ARFIMA: ভগ্নাংশিক সমাকলিত ARMA মডেল
ক্ষেত্র	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1994	1980
প্রবর্তক	Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)	Granger & Joyeux (1980); Hosking (1981)
ধরন	Nonlinear time-series regime-switching model	Long-memory time series model
মৌলিক উৎস	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.
অপর নাম	smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR	fractionally integrated ARMA, long-memory time series model, ARFIMA / FIGARCH, fractional differencing model
সম্পর্কিত	4	5
সারসংক্ষেপ	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.