Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Байесов модел на структурен векторна авторегресия (B-SVAR)× | Байесов модел за корекция на грешки във векторна форма (Bayesian VECM)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1998–2005 | 2002–2005 |
| Създател≠ | Sims & Zha (1998); Uhlig (2005) for sign-restriction identification | Kleibergen & Paap; Villani |
| Тип≠ | Structural multivariate time-series model | Bayesian multivariate time series model |
| Основополагащ източник≠ | Sims, C. A., & Zha, T. (1998). Bayesian methods for dynamic multivariate models. International Economic Review, 39(4), 949–968. DOI ↗ | Kleibergen, F., & Paap, R. (2002). Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration. Journal of Econometrics, 111(2), 223–249. DOI ↗ |
| Други названия | Bayesian SVAR, B-SVAR, Bayesian structural VAR, Bayesian identified VAR | Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction |
| Свързани≠ | 6 | 5 |
| Резюме≠ | The Bayesian Structural Vector Autoregression model combines the structural identification of SVAR with Bayesian prior distributions over parameters. It estimates causal impulse responses between multiple time series while incorporating prior economic knowledge and producing full posterior uncertainty bands rather than point estimates alone. | 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. |
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