השוואת שיטות

	רגרסיית ריבועים פחותים רגילים (OLS)	מודל STAR (Smooth Transition Autoregressive)
תחום	אקונומטריקה	אקונומטריקה
משפחה	Regression model	Regression model
שנת המקור	2019	1994
הוגה השיטה	Wooldridge (textbook treatment); classical least squares	Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)
סוג	Linear regression	Nonlinear time-series regime-switching model
מקור מכונן	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218.
כינויים	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR
קשורות	5	4
תקציר	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).	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.