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。
| 非线性自回归积分移动平均模型× | 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 | 计量经济学 |
| 方法族 | Regression model | Regression model | Regression model |
| 起源年份≠ | 1978-1994 | 1970 | 2005 |
| 提出者≠ | Howell Tong (SETAR/TAR framework); Timo Terasvirta (STAR extensions) | George Box and Gwilym Jenkins | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| 类型≠ | Nonlinear time series model | Time series forecasting model | Multivariate time-series model |
| 开创性文献≠ | Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 9780198522249 | 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 | nonlinear ARIMA, NARIMA, nonlinear time series model, nonlinear Box-Jenkins model | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| 相关≠ | 3 | 6 | 4 |
| 摘要≠ | The Nonlinear ARIMA model extends the classical Box-Jenkins ARIMA framework by allowing the conditional mean of a time series to depend on past values and past errors through a nonlinear function. It encompasses families such as Threshold AR (TAR/SETAR), Smooth Transition AR (STAR/LSTAR/ESTAR), and Markov-switching models, capturing asymmetric dynamics, regime changes, and business-cycle asymmetries that linear ARIMA cannot represent. | 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). |
| ScholarGate数据集 ↗ |
|
|
|