How to use FSBforecast Excel add-in for regression analysis

If the Time Series Data box is checked on the regression input panel, then many additional transformations are
available which are specific to time series, such as computing lagged values, or changes from one period to
another, or percentage changes from one period to another, or adjusting for inflation using a fixed rate of
deflation:
Additional transformations that are
specific to time series data: lags,
differences, and deflation. These are only
available when the “Time Series Data” box
is checked on the regression input panel.
Scaling of variables: The coefficients in the regression equation and regression summary table are displayed in
fixed format with 3 decimal places. Normally this is fine for a wide range of units of measurement, but if your
dependent and independent variables are measured in units that are “poorly scaled” relative to each other (e.g.
one measured in dollars and another measured in millions or billions of dollars), the coefficients may end up
displaying as zeros in 3‐decimal‐place format because their estimated values are less than 0.0005, even though
they are statistically significant. Keep in mind that the value of a regression coefficient is measured in “units of Y
per unit of X”, whatever those units may be. If you are puzzled to find zeros or very small numbers in the model
equation or table of regression coefficients, when the model otherwise seems reasonable, you should consider
rescaling some of the variables. For example, if an independent variable has a coefficient that is displayed as zero
despite being statistically significant (as indicated by a large t‐stat and a small P‐value), consider rescaling it in
thousands of its original units, so that its values are smaller by a factor of 1000, which will increase its estimated
coefficient by the same factor while leaving the t‐stat and P‐value unaffected. Alternatively, you might rescale the
dependent variable so that its values are larger rather than smaller. In the car data example above, the
coefficients of RevsPerMile and Weight were on the order of 0.002 and ‐0.008 respectively, so they were
displayed with only one significant digit of precision. Some re‐scaling of variables might be helpful there. For
example, you could create a new dependent variable called GallonsPer100Miles by multiplying GallonsPerMile by
100. This would increase the values of all the estimated coefficients by a factor of 100, other things being equal.
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