手法を比較

	滑らかな遷移自己回帰（STAR）モデル	ARFIMA: 階差次数が分数であるARMAモデル	分位点回帰
分野	計量経済学	計量経済学	計量経済学
系統	Regression model	Regression model	Regression model
提唱年	1994	1980	1978
提唱者	Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)	Granger & Joyeux (1980); Hosking (1981)	Koenker & Bassett
種類	Nonlinear time-series regime-switching model	Long-memory time series model	Conditional quantile regression
原典	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.
別名	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
関連	4	5	5
概要	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.