方法对比
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| 贝叶斯向量误差修正模型 (Bayesian VECM)× | 贝叶斯 ARIMA 模型× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2002–2005 | 1970s (ARIMA); Bayesian extension prominent from 1990s |
| 提出者≠ | Kleibergen & Paap; Villani | Pole, West & Harrison (Bayesian treatment); Box & Jenkins (ARIMA foundation) |
| 类型≠ | Bayesian multivariate time series model | Bayesian time series model |
| 开创性文献≠ | Kleibergen, F., & Paap, R. (2002). Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration. Journal of Econometrics, 111(2), 223–249. DOI ↗ | Pole, A., West, M., & Harrison, J. (1994). Applied Bayesian Forecasting and Time Series Analysis. Chapman & Hall. ISBN: 978-0412416903 |
| 别名 | Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction | Bayesian ARIMA, BARIMA, Bayesian Box-Jenkins model, Bayesian integrated time series model |
| 相关≠ | 5 | 6 |
| 摘要≠ | The Bayesian VECM combines the classical Vector Error Correction Model — which captures both short-run dynamics and long-run cointegrating relationships among non-stationary multivariate time series — with Bayesian prior distributions over the cointegrating rank and coefficient matrices. This allows principled uncertainty quantification, incorporation of economic theory as priors, and coherent inference even in small samples. | The Bayesian ARIMA model combines the classical Box-Jenkins ARIMA framework with Bayesian inference. Instead of obtaining single point estimates for autoregressive and moving average parameters, it places prior distributions over them and uses observed data to update beliefs into a full posterior distribution, enabling coherent uncertainty quantification and probabilistic forecasting. |
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