방법 비교

	평활 전환 자기회귀 (STAR) 모형	ARFIMA: 분수 차분 ARMA 모형	패널 벡터 자기회귀 (패널 VAR)
분야	계량경제학	계량경제학	계량경제학
계열	Regression model	Regression model	Regression model
기원 연도	1994	1980	1988
창시자	Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)	Granger & Joyeux (1980); Hosking (1981)	Holtz-Eakin, Newey & Rosen
유형	Nonlinear time-series regime-switching model	Long-memory time series model	Panel vector autoregression
원전	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.	Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395.
별칭	smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR	fractionally integrated ARMA, long-memory time series model, ARFIMA / FIGARCH, fractional differencing model	PVAR, panel vector autoregression, Panel VAR (PVAR)
관련	4	5	3
요약	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.	Panel VAR extends the vector autoregression model to panel data, modelling the dynamic interactions among several variables while controlling for cross-unit heterogeneity through fixed effects. It was introduced by Holtz-Eakin, Newey and Rosen in 1988 and produces impulse-response functions and variance decompositions at the panel level.