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평활 전환 자기회귀 (STAR) 모형×최소제곱법(OLS) 회귀×패널 벡터 자기회귀 (패널 VAR)×
분야계량경제학계량경제학계량경제학
계열Regression modelRegression modelRegression model
기원 연도199420191988
창시자Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)Wooldridge (textbook treatment); classical least squaresHoltz-Eakin, Newey & Rosen
유형Nonlinear time-series regime-switching modelLinear regressionPanel vector autoregression
원전Teräsvirta, 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: 978-1337558860Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395. DOI ↗
별칭smooth transition autoregressive model, LSTAR, ESTAR, logistic STARordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonuPVAR, panel vector autoregression, Panel VAR (PVAR)
관련453
요약The 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).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.
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