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。
| ARIMA（自回归积分滑动平均）模型× | 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≠ | 2015 | 1986 |
| 提出者≠ | Box & Jenkins (Box-Jenkins methodology) | Litterman (1986); Bańbura, Giannone & Reichlin (2010) |
| 类型≠ | Univariate time-series model | Bayesian multivariate time-series model |
| 开创性文献≠ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | 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≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR) |
| 相关 | 5 | 5 |
| 摘要≠ | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). | 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. |
| ScholarGate数据集 ↗ |
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