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| Modello Autoregressivo a Transizione Liscia (STAR)× | Vector Autoregressione su Dati Panel (Panel VAR)× | Regressione quantilica× | |
|---|---|---|---|
| Campo | Econometria | Econometria | Econometria |
| Famiglia | Regression model | Regression model | Regression model |
| Anno di origine≠ | 1994 | 1988 | 1978 |
| Ideatore≠ | Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002) | Holtz-Eakin, Newey & Rosen | Koenker & Bassett |
| Tipo≠ | Nonlinear time-series regime-switching model | Panel vector autoregression | Conditional quantile regression |
| 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 ↗ | Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Alias≠ | smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR | PVAR, panel vector autoregression, Panel VAR (PVAR) | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Correlati≠ | 4 | 3 | 5 |
| 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. | 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. | 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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