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| 평활 전환 자기회귀 (STAR) 모형× | 최소제곱법(OLS) 회귀× | |
|---|---|---|
| 분야 | 계량경제학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1994 | 2019 |
| 창시자≠ | Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002) | Wooldridge (textbook treatment); classical least squares |
| 유형≠ | Nonlinear time-series regime-switching model | Linear regression |
| 원전≠ | Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 별칭≠ | smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 관련≠ | 4 | 5 |
| 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. | 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). |
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