方法对比
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| 自回归积分滑动平均模型 (ARIMA)× | 向量自回归 (VAR) 模型× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1970 | 2005 |
| 提出者≠ | George Box and Gwilym Jenkins | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| 类型≠ | Time series forecasting model | Multivariate time-series model |
| 开创性文献≠ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| 别名 | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| 相关≠ | 6 | 4 |
| 摘要≠ | The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics. | 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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