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| ARIMA (Autoregressive Integrated Moving Average) 모형× | 마르코프 정권 전환 모형 (MS-AR / MS-VAR)× | |
|---|---|---|
| 분야 | 계량경제학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2015 | 1989 |
| 창시자≠ | Box & Jenkins (Box-Jenkins methodology) | Hamilton (1989); Kim & Nelson (1999) |
| 유형≠ | Univariate time-series model | Regime-switching time series model |
| 원전≠ | 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 | 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 ↗ |
| 별칭≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR |
| 관련 | 5 | 5 |
| 요약≠ | 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). | 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. |
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