Autoregressive model are notated using the AR§ format. Where p = number of lagged values the AR model will include as independent variables. AR (2) would be two independent variables. lagging the same variable at different time periods does not create another independent variable.
So it depends on whether you really want to model Xt based on the two previous periods or simply want to make a seasonal adjustment?
So if I had an AR(2) model, and found autocorrelation due to the fact that returns were related to returns 2 months prior, a seasonality adjusted AR(2) model should look like:
AR(4) model with Q4 seasonal lag y(t) = b0 + b1* y(t-1) + b2*y(t-2) + b3*y(t-3) + b4*y(t-4) + b_s*y(t-4) + e(t)
It seems that we add the seasonal lag to whatever AR() model! I’m not sure I understand how adding the same value twice makes sense, but I’ll take that for now.