If this has been asked elsewhere, I apologize - I've looked around and while there is lots of discussion about selecting lag order for VAR models, I haven't found anything addressing my specific question.
It seems that with VAR models there is a consensus that selecting a lag order to match the data's seasonality is usually appropriate (so lag=4 for quarterly data, 12 for monthly data). I am using weekly time series data that exhibits strong seasonality, so based on this point I suppose I could set the lag order to 52 - but my instinct is that this would be absolutely ridiculous, lead to over-fitting on the data, and not produce a reliable forecast.
I'm not going to pretend to be a PhD researcher or anything; I've just been relying on the VARselect function in R which recommends a lag based off of AIC (or other specified criteria). I have lag.max set to 10, and am using the lag recommended by AIC (lag=6). This seems reasonable to me, but I don't want to make a theoretical mistake by not using a lag that aligns with the data's seasonality and am just looking for some confirmation that this is appropriate. Thanks!