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| Model średniej ruchomej (MA) w ujęciu bayesowskim× | Model średniej ruchomej (MA)× | |
|---|---|---|
| Dziedzina | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 1970s–1997 | 1970 |
| Twórca≠ | Bayesian framework applied to Box-Jenkins MA models; West & Harrison (1997) canonical treatment | Box and Jenkins |
| Typ≠ | Bayesian time series model | Linear time series model |
| Źródło pierwotne≠ | West, M., & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259 | Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744 |
| Inne nazwy | Bayesian MA, Bayesian moving average, BMA time series, MA model with Bayesian estimation | MA model, MA(q) process, moving-average process, Box-Jenkins MA |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | 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 Moving Average model of order q — written MA(q) — expresses the current value of a time series as a linear combination of the current and past random shocks (innovations). Unlike the AR model which uses lagged values of the series itself, the MA model uses lagged error terms, making it well-suited for capturing short-lived disturbances that dissipate over q periods. |
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