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| Model vektorovej autoregresie (VAR)× | Model ARIMA (Autoregressive Integrated Moving Average)× | Regresia metódou najmenších štvorcov (OLS)× | |
|---|---|---|---|
| Odbor | Ekonometria | Ekonometria | Ekonometria |
| Rodina | Regression model | Regression model | Regression model |
| Rok vzniku≠ | 2005 | 2015 | 2019 |
| Tvorca≠ | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition | Box & Jenkins (Box-Jenkins methodology) | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Multivariate time-series model | Univariate time-series model | Linear regression |
| Pôvodný zdroj≠ | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Ďalšie názvy≠ | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Príbuzné≠ | 4 | 5 | 5 |
| Zhrnutie≠ | 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). | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). | 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). |
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