Modeling and Multivariate Methods - SAS

364 Performing Time Series Analysis Chapter 14
Modeling Reports
t Ratio lists the test statistics for the hypotheses that each parameter is zero. It is the ratio of the parameter
estimate to its standard error. If the hypothesis is true, then this statistic has an approximate Student’s
t-distribution. Looking for a t-ratio greater than 2 in absolute value is a common rule of thumb for
judging significance because it approximates the 0.05 significance level.
Prob>|t| lists the observed significance probability calculated from each t-ratio. It is the probability of
getting, by chance alone, a t-ratio greater (in absolute value) than the computed value, given a true
hypothesis. Often, a value below 0.05 (or sometimes 0.01) is interpreted as evidence that the parameter
is significantly different from zero.
The Parameter Estimates table also gives the Constant Estimate, for models that contain an intercept or
mean term. The definition of the constant estimate is given under “ARIMA Model” on page 365.
Forecast Plot
Residuals
Each model has its own Forecast plot. The Forecast plot shows the values that the model predicts for the
time series. It is divided by a vertical line into two regions. To the left of the separating line the
one-step-ahead forecasts are shown overlaid with the input data points. To the right of the line are the future
values forecast by the model and the confidence intervals for the forecasts.
You can control the number of forecast values by changing the setting of the Forecast Periods box in the
platform launch dialog or by selecting Number of Forecast Periods from the Time Series drop-down
menu. The data and confidence intervals can be toggled on and off using the Show Points and Show
Confidence Interval commands on the model’s popup menu.
The graphs under the residuals section of the output show the values of the residuals based on the fitted
model. These are the actual values minus the one-step-ahead predicted values. In addition, the
autocorrelation and partial autocorrelation of these residuals are shown. These can be used to determine
whether the fitted model is adequate to describe the data. If it is, the points in the residual plot should be
normally distributed about the zero line and the autocorrelation and partial autocorrelation of the residuals
should not have any significant components for lags greater than zero.