Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель Марковских переключений режимов (MS-AR / MS-VAR)× | Модель ARIMA (авторегрессионная интегрированная скользящая средняя)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1989 | 2015 |
| Автор метода≠ | Hamilton (1989); Kim & Nelson (1999) | Box & Jenkins (Box-Jenkins methodology) |
| Тип≠ | Regime-switching time series model | Univariate time-series model |
| 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 ↗ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 |
| Другие названия≠ | regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Связанные | 5 | 5 |
| Сводка≠ | The Markov regime-switching model lets the parameters of a time series change probabilistically across hidden regimes governed by a Markov chain. Introduced by Hamilton (1989) and developed further by Kim and Nelson (1999), it automatically detects business-cycle phases such as expansions and contractions. | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). |
| ScholarGateНабор данных ↗ |
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