Content deleted Content added
No edit summary |
|||
Line 91:
===Estimating coefficients===
ARMA models in general can be, after choosing ''p'' and ''q'', fitted by [[least squares]] regression to find the values of the parameters which minimize the error term. It is generally considered good practice to find the smallest values of ''p'' and ''q'' which provide an acceptable fit to the data. For a pure AR model the [[AR model#Calculation of the AR parameters|Yule-Walker equations]] may be used to provide a fit.
Unlike other methods of regression (i.e. OLS, 2SLS, etc.) often employed in econometric analysis, ARMA model outputs are used primarily for the cases of forecasting time-series data. Their coefficients are then as such only utilized for prediction. Other areas of econometrics look at the causal inference, time-series forecasting using ARMA is not. The coefficients should then only be seen as useful for predictive modelling.
=== Implementations in statistics packages ===
| |||