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The Model Summary table contains fit statistics for the model. In the formulas below, n is the length of the series and k is the number of fitted parameters in the model.
The unconditional sum of squares (SSE) divided by the number of degrees of freedom (n – k). The variance estimate is computed as SSE / (n – k). This is the sample estimate of the variance of the random shocks at, described in the section ARIMA Model.
The adjusted R2 is computed as follows:
Note: The φ and θ operators are defined in the section ARIMA Model.
The name of the parameter, which are described in the sections for each model type. Some models contain an intercept or mean term. In those models, the related constant estimate is also shown. The definition of the constant estimate is given under the description of ARIMA models.
The test statistics for the hypotheses that each parameter is zero. The test statistic for a parameter 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 for judging significance because it approximates the 0.05 significance level.
The observed p-value calculated for each parameter. The p-value is the probability of getting, by chance alone, a t-ratio greater (in absolute value) than the computed value, given a true hypothesis.

Help created on 3/19/2020