مقایسهٔ روشها
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| مدلهای حافظه بلندمدت (ARFIMA, FIGARCH)× | رگرسیون حداقل مربعات معمولی (OLS)× | |
|---|---|---|
| حوزه≠ | مالی | اقتصادسنجی |
| خانواده | Regression model | Regression model |
| سال پیدایش≠ | 1980 | 2019 |
| پدیدآور≠ | Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH) | Wooldridge (textbook treatment); classical least squares |
| نوع≠ | Fractionally integrated time series model | Linear regression |
| منبع بنیادین≠ | 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 |
| نامهای دیگر≠ | ARFIMA, FIGARCH, fractionally integrated models, fractional integration | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| مرتبط≠ | 4 | 5 |
| خلاصه≠ | 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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