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	Smooth Transition Autoregressive (STAR) modell	Vanligaste minsta kvadratmetoden (OLS) Regression	Kvantilregression
Ämnesområde	Ekonometri	Ekonometri	Ekonometri
Familj	Regression model	Regression model	Regression model
Ursprungsår	1994	2019	1978
Upphovsperson	Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett
Typ	Nonlinear time-series regime-switching model	Linear regression	Conditional quantile regression
Ursprungskälla	Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
Närliggande	4	5	5
Sammanfattning	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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.