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| Модел на авторегресия с плавен преход (STAR)× | Квантилна регресия× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1994 | 1978 |
| Създател≠ | Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002) | Koenker & Bassett |
| Тип≠ | Nonlinear time-series regime-switching model | Conditional quantile regression |
| Основополагащ източник≠ | 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 ↗ |
| Други названия≠ | smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Свързани≠ | 4 | 5 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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