The t-test, also known as the t-statistic, indicates whether or not an independent variable is correlated to the dependent variable; that is, it tells you whether or not the independent variable helps to explain the dependent variable and, therefore, whether or not you should leave the independent variable in the model.
The t-statistic says nothing about the significance of an explanatory variable's magnitude or impact. In other words, a t-statistic of say 4.6 is no more significant than a 2.4; it only means the independent variables associated with those t-statistics are significant in explaining the variation in the dependent variable. The magnitude of that relationship is measured by the coefficient of the independent variable and its unit of measure.
The rule of thumb for a t-statistic for determining whether a coefficient of an independent variable is significantly correlated to the dependent variable at a 95% confidence level is a +/- 2.0. However, empirical testing has proven that a t-statistic of +/- 1.4 or greater is structurally significant at the 90% confidence level.
SAP therefore recommends that you leave explanatory variables with a t-statistic of +/- 1.4 or greater in the model. If some of your independent variables have a t-statistic of less than +/- 1.4, run an ex-post forecast first with and then without them. If the forecast error is lower with the explanatory variables, leave them in. However, if the t-statistic for these independent variables is below +/- 1.4 your structural analysis will no longer be valid.
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