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| Modèle autorégressif à transition lisse (STAR)× | Régression quantile× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1994 | 1978 |
| Auteur d'origine≠ | Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002) | Koenker & Bassett |
| Type≠ | Nonlinear time-series regime-switching model | Conditional quantile regression |
| Source fondatrice≠ | Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Alias≠ | smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Apparentées≠ | 4 | 5 |
| Résumé≠ | 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. | 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. |
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