手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| 長期記憶モデル（ARFIMA、FIGARCH）× | 最小二乗法 (OLS) 回帰× | |
|---|---|---|
| 分野≠ | ファイナンス | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 1980 | 2019 |
| 提唱者≠ | Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH) | Wooldridge (textbook treatment); classical least squares |
| 種類≠ | Fractionally integrated time series model | Linear regression |
| 原典≠ | 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 |
| 別名≠ | ARFIMA, FIGARCH, fractionally integrated models, fractional integration | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 関連≠ | 4 | 5 |
| 概要≠ | 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). |
| ScholarGateデータセット ↗ |
|
|