AUTOREGRESSIVE MOVING AVERAGE MODEL Meaning and
Definition
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The autoregressive moving average model (ARMA) is a statistical model used to analyze time series data. It combines two components, namely the autoregressive (AR) and moving average (MA) models, to capture the characteristics of the data.
The autoregressive component involves predicting the current value of a variable based on its own past values. It assumes that the current value is linearly related to the previous values of the variable. The AR model considers how the dependent variable is influenced by its own lagged values, with the degree of influence determined by the order of the model (represented by the "p" parameter). Higher order AR models suggest a greater impact of past values on the present value.
The moving average component, on the other hand, focuses on modeling the relationship between the dependent variable and past errors or residuals. It assumes that the current value is a linear combination of the error terms from previous observations. Similar to the AR model, the MA model also has an order (represented by the "q" parameter) that determines the number of lagged error terms used for prediction.
Combining the AR and MA models into an ARMA model allows for the analysis and prediction of time series data, taking into account both the influence of past values and the relationship with past errors. The parameters p and q determine the order of both components, and they can be estimated using statistical methods such as maximum likelihood estimation or least squares.
