Working day adjusting variable and Autocorrelation

[relaxdave]

I am estimating monthly GDP, which works pretty good until I control for the number of working days per month.

When I include this variable into the models (AR, DL and ARMA), I get serious problems with autocorrelation in the residuals, the R2 is higher though.

Does anybody have an idea why this is the case and how else I could control for working days?

Calendar adjustment would be very costly since I want to pursue forecasts.

Thank you in advance!

Dave

[trubador]

Autoregressive structure of these models may create such problems. If you really "do not" want to pre-adjust your variable, then you can use working days series as a factor for the dependent variable instead of an exogenous variable. You may either use (Y/wdays) or (Y*@max(wdays)/wdays) as your dependent variable...

[relaxdave]

Thank you, Trubador!

I tried transforming the variable the way you described it and in the end I could get rid of the serial correlation by adding more lagged endogenous variables.

I would like to estimate seasonally adjusted data as well - the only problem I have is, that I have to forecast the seasonally UNadjusted values later and I don't know how to unadjust the forecasted data. Do you have any suggestions?

Thanks, Dave

[trubador]

You can directly model the seasonality along with your model via using additional AR/SAR lags or creating seperate dummy variables. Something along the following lines:

Code: Select all

Y*wdays/@max(wdays) c ar(1) ar(12)

or

Code: Select all

Y*wdays/@max(wdays) ar1 @expand(@month)

If you want to drop insignificant seasonal dummies, then you can use @seas() command afterwards.