Regression modelEconometrics / time series

Bayesian ARMA Model

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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Sources

  1. Geweke, J., & Meese, R. (1981). Estimating regression models of finite but unknown order. International Economic Review, 22(1), 55–70. link
  2. Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021

Related methods

Referenced by

ScholarGateBayesian ARMA model (Bayesian Autoregressive Moving Average Model). Retrieved 2026-06-04 from https://scholargate.app/en/econometrics/bayesian-arma-model