Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo Autorregresivo Bayesiano (AR)× | Modelo ARMA (Autoregresivo de Media Móvil)× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1971 | 1970 |
| Autor original≠ | Arnold Zellner; foundational Bayesian time-series work by West & Harrison | George E. P. Box and Gwilym M. Jenkins |
| Tipo≠ | Bayesian time-series model | Time series model |
| Fuente seminal≠ | 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 ↗ |
| Alias | Bayesian autoregressive model, BAR model, Bayesian AR, Bayesian time-series autoregression | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Relacionados≠ | 6 | 5 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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