方法对比
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| 非线性自回归 (NAR) 模型× | 自回归模型 (AR)× | 非线性自回归分布式滞后 (NARDL) 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≠ | 1978-1990 | 1970s (popularised 1976) | 2014 |
| 提出者≠ | Tong, H. (threshold AR); Terasvirta, T. (STAR variant) | George E. P. Box and Gwilym M. Jenkins | Shin, Yu & Greenwood-Nimmo |
| 类型≠ | Nonlinear time series model | Time series model | Nonlinear cointegration model |
| 开创性文献≠ | Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 9780198522201 | Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0816211043 | Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281–314). Springer. link ↗ |
| 别名 | NAR model, nonlinear autoregression, NLAR, threshold autoregressive model | AR model, AR(p) model, autoregression, AR process | NARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model |
| 相关≠ | 6 | 6 | 5 |
| 摘要≠ | The Nonlinear AR model extends the classical autoregressive framework by allowing the mapping from past values to the current value to follow an arbitrary or regime-switching nonlinear function. Major families include the Self-Exciting Threshold AR (SETAR), Smooth Transition AR (STAR), and neural network AR, each capturing different forms of asymmetry, regime shifts, or smooth nonlinear dynamics in univariate time series. | An autoregressive model of order p — AR(p) — expresses the current value of a time series as a linear function of its own p most recent past values plus a white-noise error. It is the building block of the Box-Jenkins family of time-series models and is widely used for forecasting stationary economic and financial series. | The Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing the regressor into cumulative positive and negative partial sums, it tests whether increases and decreases in a variable exert different effects on the outcome — a feature especially relevant in financial and energy economics where positive and negative shocks rarely cancel out symmetrically. |
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