方法对比
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| 贝叶斯向量误差修正模型 (Bayesian VECM)× | 向量误差修正模型 (VECM)× | |
|---|---|---|
| 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 | 1987 |
| 提出者≠ | Kleibergen & Paap; Villani | Robert F. Engle and Clive W. J. Granger |
| 类型≠ | Bayesian multivariate time series model | Multivariate 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 ↗ | Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. DOI ↗ |
| 别名 | Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction | VECM, error correction VAR, cointegrated VAR, vector equilibrium correction model |
| 相关 | 5 | 5 |
| 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 Vector Error Correction Model extends the Vector Autoregression (VAR) framework to a system of variables that share one or more long-run equilibrium relationships. It jointly models short-run dynamics and the speed at which each variable corrects back toward equilibrium after a shock, making it the standard tool for analysing cointegrated multivariate time series. |
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