方法对比
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| 贝叶斯增广迪基-富勒单位根检验× | 贝叶斯向量自回归模型 (BVAR)× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1991–1992 | 1984 |
| 提出者≠ | Sims & Uhlig (1991); Koop, Osiewalski & Steel (1992) | Doan, Litterman & Sims |
| 类型≠ | Bayesian hypothesis test | Multivariate time-series model |
| 开创性文献≠ | Sims, C. A., & Uhlig, H. (1991). Understanding unit rooters: A helicopter tour. Econometrica, 59(6), 1591–1599. DOI ↗ | Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100. DOI ↗ |
| 别名 | Bayesian ADF test, Bayesian unit root test, Bayesian Dickey-Fuller, BADF | BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model |
| 相关≠ | 6 | 5 |
| 摘要≠ | The Bayesian Augmented Dickey-Fuller (BADF) unit root test re-frames the classical ADF test within a Bayesian framework. Rather than computing a frequentist p-value, it quantifies evidence for or against a unit root by comparing posterior probabilities or Bayes factors under the null (unit root) and alternative (stationarity) hypotheses, incorporating prior beliefs about the autoregressive parameter. | The Bayesian Vector Autoregression (BVAR) model extends the classical VAR framework by incorporating prior beliefs about the model coefficients. Priors — most commonly the Minnesota prior — shrink VAR coefficients toward economically sensible values, dramatically reducing overfitting and improving out-of-sample forecast accuracy even when the number of variables is large. |
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