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| Regressió per Mínims Quadrats Ordinàris (MQO)× | Vector autoregressiu amb llindar i transició suau (TVAR / STVAR)× | Model d'Autoregressió Vectorial (VAR)× | |
|---|---|---|---|
| Camp | Econometria | Econometria | Econometria |
| Família | Regression model | Regression model | Regression model |
| Any d'origen≠ | 2019 | 1998 | 2005 |
| Autor original≠ | Wooldridge (textbook treatment); classical least squares | Tsay (multivariate threshold modelling) | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| Tipus≠ | Linear regression | Nonlinear multivariate time-series model | Multivariate time-series model |
| Font seminal≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Tsay, R. S. (1998). Testing and Modeling Multivariate Threshold Models. Journal of the American Statistical Association, 93(443), 1188-1202. DOI ↗ | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| Àlies≠ | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | TVAR, STVAR, regime-switching VAR, threshold VAR | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| Relacionats≠ | 5 | 5 | 4 |
| Resum≠ | 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). | Threshold VAR and Smooth-Transition VAR are nonlinear multivariate time-series models in which the coefficients of a vector autoregression switch between regimes according to a threshold variable. Building on Tsay's 1998 treatment of multivariate threshold models, they capture different dynamic structures across phases such as the business cycle, financial crises, or policy differences. | Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005). |
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