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| Байесов модел на авторегресия (AR)× | АРСС модел (авторегресионна плъзгаща се средна)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1971 | 1970 |
| Създател≠ | Arnold Zellner; foundational Bayesian time-series work by West & Harrison | George E. P. Box and Gwilym M. Jenkins |
| Тип≠ | Bayesian time-series model | Time series model |
| Основополагащ източник≠ | Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376 | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Други названия | Bayesian autoregressive model, BAR model, Bayesian AR, Bayesian time-series autoregression | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Свързани≠ | 6 | 5 |
| Резюме≠ | 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. | The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting. |
| ScholarGateНабор от данни ↗ |
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