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| Bayesian Autoregressive (AR) Modell× | Autoregressives Modell (AR)× | |
|---|---|---|
| Fachgebiet | Ökonometrie | Ökonometrie |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1971 | 1970s (popularised 1976) |
| Urheber≠ | Arnold Zellner; foundational Bayesian time-series work by West & Harrison | George E. P. Box and Gwilym M. Jenkins |
| Typ≠ | Bayesian time-series model | Time series model |
| Wegweisende Quelle≠ | Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376 | Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0816211043 |
| Aliasnamen | Bayesian autoregressive model, BAR model, Bayesian AR, Bayesian time-series autoregression | AR model, AR(p) model, autoregression, AR process |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | 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. | An autoregressive model of order p — AR(p) — expresses the current value of a time series as a linear function of its own p most recent past values plus a white-noise error. It is the building block of the Box-Jenkins family of time-series models and is widely used for forecasting stationary economic and financial series. |
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