Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Modele cu memorie lungă (ARFIMA, FIGARCH)× | Regresia prin metoda celor mai mici pătrate ordinare (OLS)× | |
|---|---|---|
| Domeniu≠ | Finanțe | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1980 | 2019 |
| Autorul original≠ | Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH) | Wooldridge (textbook treatment); classical least squares |
| Tip≠ | Fractionally integrated time series model | Linear regression |
| Sursa seminală≠ | 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 |
| Denumiri alternative≠ | ARFIMA, FIGARCH, fractionally integrated models, fractional integration | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Înrudite≠ | 4 | 5 |
| Rezumat≠ | 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). |
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