User’s Guide : Basic Single Equation Analysis : Time Series Regression
Time Series Regression
In this section, we turn our attention to the analysis of single equation models for time series data, focusing on the estimation of Autoregressive-Moving Average (ARMA), Autoregressive-Integrated-Moving Average (ARIMA), and Autoregressive-Fractionally Integrated-Moving Average (ARFIMA) specifications, and the computation of equation diagnostics for these models.
Before turning to the EViews implementation of these features, we provide brief background for the models and related diagnostics. Those desiring additional detail are encouraged to consult one or more of the many book length treatments of time series methods (Box, Jenkins, and Reinsel, 2008; Hamilton, 1994).
Related topics are discussed elsewhere in this volume; see, for example, Univariate Time Series Analysis, Vector Autoregression and Error Correction Models, State Space Models and the Kalman Filter for material on additional time series topics.