Regression modelEconometrics / time series
ARMA Model (Autoregressive Moving Average)
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.
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Sources
- Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗
- Brockwell, P. J., & Davis, R. A. (2002). Introduction to Time Series and Forecasting (2nd ed.). Springer. ISBN: 978-0387953519
Related methods
Referenced by
ARIMA modelAutoregressive modelBayesian AR modelBayesian ARMA modelFourier AR ModelFourier ARMA modelMoving Average ModelNonlinear AR ModelNonlinear ARMA modelNonlinear MA modelPanel ARMA modelQuantile-on-Quantile RegressionRobust AR modelRobust ARMA ModelRobust MA modelSARIMA modelStructural VARTime-varying parameter ARMA modelTime-varying parameter MA modelVector Autoregression