Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Моделі довгої пам'яті (ARFIMA, FIGARCH)× | Регресія звичайно найменших квадратів (ЗНК)× | |
|---|---|---|
| Галузь≠ | Фінанси | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1980 | 2019 |
| Автор методу≠ | Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH) | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Fractionally integrated time series model | Linear regression |
| Основоположне джерело≠ | Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15-29. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Інші назви≠ | ARFIMA, FIGARCH, fractionally integrated models, fractional integration | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Пов'язані≠ | 4 | 5 |
| Підсумок≠ | Long-memory models are fractional-integration methods that capture genuine long memory through a hyperbolically decaying autocorrelation structure. ARFIMA, introduced by Granger and Joyeux (1980), models long memory in return series, while FIGARCH, introduced by Baillie, Bollerslev and Mikkelsen (1996), captures long memory in volatility series; the parameter d measures the degree of fractional integration. | 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). |
| ScholarGateНабір даних ↗ |
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