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| 贝叶斯 Phillips-Perron 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≠ | 1988 / early 1990s | 1984 |
| 提出者≠ | Phillips & Perron (classical test, 1988); Bayesian framework: Sims & Uhlig (1991) | Doan, Litterman & Sims |
| 类型≠ | Unit root test (Bayesian) | Multivariate time-series model |
| 开创性文献≠ | Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346. 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 PP test, Bayesian Phillips-Perron test, Bayesian nonparametric unit root test, Bayes PP unit root | BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model |
| 相关 | 5 | 5 |
| 摘要≠ | The Bayesian Phillips-Perron unit root test combines the nonparametric long-run variance correction of the classical Phillips-Perron test with a Bayesian inferential framework. Instead of a p-value, it yields a posterior probability or Bayes factor quantifying evidence for or against a unit root, allowing researchers to incorporate prior economic knowledge and obtain probability statements directly about the persistence of a time series. | 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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