方法对比
并排查看您选择的方法;存在差异的行会高亮显示。
| Johansen协整检验与向量误差修正模型× | ARIMA(自回归积分滑动平均)模型× | 向量自回归 (VAR) 模型× | |
|---|---|---|---|
| 领域≠ | 金融学 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model | Regression model |
| 起源年份≠ | 1991 | 2015 | 2005 |
| 提出者≠ | Søren Johansen | Box & Jenkins (Box-Jenkins methodology) | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| 类型≠ | Multivariate cointegration / vector error correction model | Univariate time-series model | Multivariate time-series model |
| 开创性文献≠ | Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59(6), 1551-1580. 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 | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| 别名≠ | Johansen test, VECM, vector error correction model, multivariate cointegration | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| 相关≠ | 3 | 5 | 4 |
| 摘要≠ | The Johansen procedure is a multivariate cointegration framework, introduced by Søren Johansen in 1991, that tests for long-run equilibrium relationships among several I(1) time series. It determines how many cointegrating vectors link the series and then builds a Vector Error Correction Model (VECM) to describe the short-run dynamics around that equilibrium. | 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). | Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005). |
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