Vertaile menetelmiä

	Sileän siirtymän autoregressiivinen (STAR) malli	Paneelivektoritasausmalli (Paneeli-VAR)	Kvanttiiliregressio
Tieteenala	Ekonometria	Ekonometria	Ekonometria
Menetelmäperhe	Regression model	Regression model	Regression model
Syntyvuosi	1994	1988	1978
Kehittäjä	Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)	Holtz-Eakin, Newey & Rosen	Koenker & Bassett
Tyyppi	Nonlinear time-series regime-switching model	Panel vector autoregression	Conditional quantile regression
Alkuperäislähde	Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218.	Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Rinnakkaisnimet	smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR	PVAR, panel vector autoregression, Panel VAR (PVAR)	conditional quantile regression, regression quantiles, Kantil Regresyon
Liittyvät	4	3	5
Tiivistelmä	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.	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.	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.