Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель Баєсового ковзного середнього (MA)× | Байєсівська авторегресійна (AR) модель× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1970s–1997 | 1971 |
| Автор методу≠ | 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 |
| Основоположне джерело≠ | 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 |
| Інші назви | Bayesian MA, Bayesian moving average, BMA time series, MA model with Bayesian estimation | Bayesian autoregressive model, BAR model, Bayesian AR, Bayesian time-series autoregression |
| Пов'язані | 6 | 6 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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