Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mifumo ya Kumbukumbu-Ndefu (ARFIMA, FIGARCH)× | Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)× | |
|---|---|---|
| Nyanja≠ | Fedha | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1980 | 2019 |
| Mwanzilishi≠ | Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH) | Wooldridge (textbook treatment); classical least squares |
| Aina≠ | Fractionally integrated time series model | Linear regression |
| Chanzo asilia≠ | 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 |
| Majina mbadala≠ | ARFIMA, FIGARCH, fractionally integrated models, fractional integration | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Zinazohusiana≠ | 4 | 5 |
| Muhtasari≠ | 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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