方法对比
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| 贝叶斯向量自回归模型 (BVAR)× | 贝叶斯ARDL边界检验× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1984 | 2001 (ARDL); Bayesian extension 2010s |
| 提出者≠ | Doan, Litterman & Sims | Pesaran, Shin & Smith (ARDL framework, 2001); Bayesian adaptation by subsequent literature |
| 类型≠ | Multivariate time-series model | Cointegration / bounds testing |
| 开创性文献≠ | Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100. DOI ↗ | Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289-326. DOI ↗ |
| 别名 | BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model | Bayesian ARDL, Bayesian bounds testing approach, Bayes ARDL cointegration, Bayesian PSS bounds test |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | The Bayesian ARDL Bounds Test extends the classical Pesaran-Shin-Smith (2001) bounds testing approach to cointegration by embedding it within a Bayesian inferential framework. Instead of relying on frequentist F- and t-statistics with tabulated critical values, the researcher specifies prior distributions on the model parameters and derives posterior evidence of a long-run level relationship between variables that may be integrated of order zero or one. |
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