方法对比
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| 傅里叶自回归模型× | 自回归积分滑动平均模型 (ARIMA)× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2012 | 1970 |
| 提出者≠ | Enders & Lee | George Box and Gwilym Jenkins |
| 类型≠ | Time series model with Fourier augmentation | Time series forecasting model |
| 开创性文献≠ | Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574–599. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| 别名 | Fourier AR, trigonometric AR model, smooth transition AR with Fourier terms, FAR model | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| 相关 | 6 | 6 |
| 摘要≠ | The Fourier AR model extends the standard autoregressive specification by adding trigonometric (sine and cosine) terms to the deterministic component. This allows the model to capture smooth, gradual shifts in the mean or trend of a time series without requiring the researcher to locate or count structural break points explicitly. | 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. |
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