Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo Autorregressivo Bayesiano (AR)× | Modelo ARMA Bayesiano× | |
|---|---|---|
| Área | Econometria | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1971 | 1970s–1980s |
| Autor original≠ | Arnold Zellner; foundational Bayesian time-series work by West & Harrison | Box & Jenkins (classical ARMA); Bayesian treatment developed through work of Zellner, Geweke, and others in 1970s–1980s |
| Tipo≠ | Bayesian time-series model | Bayesian time series model |
| Fonte seminal≠ | Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376 | Geweke, J., & Meese, R. (1981). Estimating regression models of finite but unknown order. International Economic Review, 22(1), 55–70. link ↗ |
| Outros nomes | Bayesian autoregressive model, BAR model, Bayesian AR, Bayesian time-series autoregression | Bayesian ARMA, B-ARMA, Bayesian autoregressive moving average, ARMA with Bayesian inference |
| Relacionados | 6 | 6 |
| Resumo≠ | 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 Bayesian ARMA model applies Bayesian inference to the classical autoregressive moving average framework for stationary univariate time series. Rather than producing single point estimates for the AR and MA parameters, it yields full posterior distributions, naturally incorporating prior knowledge and providing coherent uncertainty quantification over forecasts and impulse responses. |
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