Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель Марковских переключений режимов для финансовых временных рядов× | Регрессия методом обыкновенных наименьших квадратов (ОНМК)× | |
|---|---|---|
| Область≠ | Финансы | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1989 | 2019 |
| Автор метода≠ | James D. Hamilton | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Markov regime-switching time-series model | Linear regression |
| Основополагающий источник≠ | Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Другие названия≠ | Markov switching model, Hamilton regime-switching model, MS-AR, hidden Markov regime model | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Связанные≠ | 1 | 5 |
| Сводка≠ | The Markov regime-switching model, introduced by James D. Hamilton in 1989, is a hidden-state time-series model in which financial series such as returns or volatility behave with different parameters across distinct economic regimes (bull/bear or high/low volatility). It is the financial application of Hamilton's MS-AR model, where an unobserved Markov state governs which parameter set is active at each point in time. | 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). |
| ScholarGateНабор данных ↗ |
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