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| Байесов модел на пълзяща средна (MA)× | Байесов модел на авторегресия (AR)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1970s–1997 | 1971 |
| Създател≠ | Bayesian framework applied to Box-Jenkins MA models; West & Harrison (1997) canonical treatment | Arnold Zellner; foundational Bayesian time-series work by West & Harrison |
| Тип≠ | Bayesian time series model | Bayesian time-series model |
| Основополагащ източник≠ | West, M., & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259 | Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376 |
| Други названия | Bayesian MA, Bayesian moving average, BMA time series, MA model with Bayesian estimation | Bayesian autoregressive model, BAR model, Bayesian AR, Bayesian time-series autoregression |
| Свързани | 6 | 6 |
| Резюме≠ | The Bayesian MA model estimates a moving average time series model within a fully Bayesian framework, placing prior distributions on the MA parameters and error variance and updating them via Bayes' theorem. This approach yields full posterior distributions over model parameters and produces probabilistic forecasts with coherent uncertainty quantification. | The Bayesian AR model estimates an autoregressive time-series process by combining a likelihood derived from the AR structure with prior distributions over the lag coefficients and error variance. Rather than producing single point estimates, it yields full posterior distributions, enabling principled uncertainty quantification and probabilistic forecasting. |
| ScholarGateНабор от данни ↗ |
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