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| Modello Autoregressivo a Transizione Liscia (STAR)× | Regression with Ordinary Least Squares (OLS)× | Vector Autoregressione su Dati Panel (Panel VAR)× | |
|---|---|---|---|
| Campo | Econometria | Econometria | Econometria |
| Famiglia | Regression model | Regression model | Regression model |
| Anno di origine≠ | 1994 | 2019 | 1988 |
| Ideatore≠ | Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002) | Wooldridge (textbook treatment); classical least squares | Holtz-Eakin, Newey & Rosen |
| Tipo≠ | Nonlinear time-series regime-switching model | Linear regression | Panel vector autoregression |
| Fonte seminale≠ | 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 | Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395. DOI ↗ |
| Alias≠ | smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | PVAR, panel vector autoregression, Panel VAR (PVAR) |
| Correlati≠ | 4 | 5 | 3 |
| Sintesi≠ | 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). | 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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