方法对比
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| 时间变化参数自回归模型 (TVP-AR)× | 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≠ | 1976–2005 | 1970 |
| 提出者≠ | Cooley & Prescott (1976); further developed by Kim & Nelson (1999) and Cogley & Sargent (2001, 2005) | George Box and Gwilym Jenkins |
| 类型≠ | Time-series model with drifting coefficients | Time series forecasting model |
| 开创性文献≠ | Cogley, T., & Sargent, T. J. (2005). Drifts and volatilities: Monetary policies and outcomes in the post WWII US. Review of Economic Dynamics, 8(2), 262-302. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| 别名 | TVP-AR, time-varying AR, state-space AR with drifting coefficients, random-walk coefficient AR | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| 相关≠ | 4 | 6 |
| 摘要≠ | The Time-Varying Parameter Autoregressive (TVP-AR) model extends the classical AR model by allowing its autoregressive coefficients to drift over time, typically as a random walk. Cast as a state-space system, the model captures gradual structural change in the dynamics of a univariate time series without imposing a fixed break date. | 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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