Hi!
1. Use UCM instead :). Maybe you are like me, and learned ARIMA modeling a long time ago and think it'll be easier. But that's not the case in the long run (a run longer than about 30 minutes, steal the examples from the documentation and start experimenting!). I have been working on similar projects and UCM does not require differencing and often provides better forecasts. Less work, better results. Also, you can use multiple CYCLE statements to take care of seasonality on yearly, quarterly, and monthly level (since you have weekly data). Another perk is that it calculates MAPE for you.
2. In one project in particular, Christmas wasn't highly significant in the analysis, but the day after the days off was (the 27th). Maybe your crosscorrelation plot shows something similar?
Also, are you sure you shouldn't add an autoregressive component? Estimate p=1 as well?
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