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。
| 状态空间模型（卡尔曼滤波器）× | 贝叶斯向量自回归 (BVAR)× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1990 | 1986 |
| 提出者≠ | Harvey; Durbin & Koopman (state space treatment); Kalman filter | Litterman (1986); Bańbura, Giannone & Reichlin (2010) |
| 类型≠ | State space time series model | Bayesian multivariate time-series model |
| 开创性文献≠ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ | Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38. DOI ↗ |
| 别名 | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) | BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR) |
| 相关≠ | 4 | 5 |
| 摘要≠ | A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases. | Bayesian VAR adds Minnesota or other prior distributions to a vector autoregressive model to control over-parameterisation. Introduced by Litterman (1986) and extended to high dimensions by Bańbura, Giannone and Reichlin (2010), it outperforms classical VAR on short series and high-dimensional macroeconomic forecasts. |
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