I'm working to develop a forecasting model for a quarterly seasonal variable (quarterly estimated individual income tax payments) using several candidates for non-seasonal independent variables (quarterly average value of S&P 500, dividend income, interest income, etc.).
I understand that I need to prewhiten the dependent and independent variables in order to generate a cross-correlation function to identify the appropriate rational transfer function for the independent variables, but the seasonal dependent variable and candidate non-seasonal independent variables require different differencing operations to be stationary. Using year-over-year differencing for the non-seasonal variables leads to over-differencing, often with AR and/or MA terms near 1.0.
Is the only solution to create a synthetic seasonally adjusted series for the dependent variable before prewhitening? Or is there another way to prewhiten variables that require different differencing opterations?