SECTION 13.4 Summary

• The first-order autoregressive model AR(1) is appropriate when successive values of a time series are linearly related. An AR(1) model can be estimated by regressing the time series on a lag one variable. In some cases, more lags can be added to the model to improve the fit.
• The partial autocorrelations function (PACF) shows adjusted autocorrelations that help in determining how many lags to add to the model.
• Lag variables can be added to trend and seasonal models to capture autocorrelation effects that are not captured by trend and seasonal indicator variables.