Critical value for 5% is -5.84, median is -4.27 (Dickey, Hasza & Fuller, 1984, table 7). DHF Conclusion: Insufficient evidence against seasonal dynamic model (seasonal unit roots). What if it’s seasonal with more lags? Multiplicative seasonal is popular. (1Bs)(11B2B2…pBp)(Yt-s)=et mean s=seasonal Step 1: Regress to Dt=YtYt-s on Dt-1, Dt-2,…Dt-p estimate Residuals are et. Using these ’s.  estimates, estimate filtered lags Ft-s= (11B2B2…pBp)(Yt-s-s) (note t-s subscript). Step 2: Regress on to Dt Ft-s, Dt-1, Dt-2, Dt-p …, get test statistic and improvements for q estimates. For visitor data with p=2:
Critical value for 5% is -5.84, median is -4.27 (Dickey, Hasza & Fuller, 1984, table 7). DHF Conclusion: Insufficient evidence against seasonal dynamic model (seasonal unit roots). What if it’s seasonal with more lags? Multiplicative seasonal is popular. (1Bs)(11B2B2…pBp)(Yt-s)=et s=seasonal mean Step 1: Regress Dt=YtYt-s on Dt-1, Dt-2,…Dt-p to estimate ’s. Residuals are et. Using these  estimates, estimate filtered lags Ft-s= (11B2B2…pBp)(Yt-s-s) (note t-s subscript). Step 2: Regress Dt on Ft-s, Dt-1, Dt-2, …, Dt-p to get test statistic and improvements for q estimates. For visitor data with p=2: