השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מודל STAR (Smooth Transition Autoregressive)× | רגרסיית ריבועים פחותים רגילים (OLS)× | אוטורגרסיה וקטורית של פאנל (Panel VAR)× | |
|---|---|---|---|
| תחום | אקונומטריקה | אקונומטריקה | אקונומטריקה |
| משפחה | Regression model | Regression model | Regression model |
| שנת המקור≠ | 1994 | 2019 | 1988 |
| הוגה השיטה≠ | Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002) | Wooldridge (textbook treatment); classical least squares | Holtz-Eakin, Newey & Rosen |
| סוג≠ | Nonlinear time-series regime-switching model | Linear regression | Panel vector autoregression |
| מקור מכונן≠ | 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 ↗ |
| כינויים≠ | 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) |
| קשורות≠ | 4 | 5 | 3 |
| תקציר≠ | 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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