Hi,   I am trying to estimate a VAR model with an exogenous variable. Y1 and Y2, for instance, auto sales and gas price, are endogenous, which I want to predict 4 periods ahead for. X is the exogenous varaible, for example, auto retail events, and 1 means there is an event that time period. The events have been planned out at the beginning of each year.    Is it possible to make forecast for the sales of the next year with the future event indicated?   Please see my example data as attached.   Thank you! ... View more

Hi SAS forecasting experts, This time I came across the problem of using weekly data in a VAR model for forecasting. I have two-year weekly sales and marketing campaign data (RAQ) and want to use VAR to run forecast. However, the sales always have spikes at the end of month. As the data is weekly, the end of month can be the fourth week of a month or (the beginning of) the fifth week. So, my question is: how to do such month-month seasonal adjustment using weekly data in order to use the seasonally adjusted data in the VAR model? The sample data is attached. Both the sales and the campaign activities are in logarithm form. Thank you! week year lsales lcampaign 0 2013 9.511629 10.94447 1 2013 9.73074 11.19503 2 2013 9.820323 11.26294 3 2013 9.888374 11.30585 4 2013 10.02167 11.24355 5 2013 9.699779 11.14156 6 2013 9.985759 11.21244 7 2013 10.14345 11.28749 8 2013 10.35993 11.2146 9 2013 9.789815 11.09255 10 2013 9.98516 11.09909 11 2013 10.05651 11.18147 12 2013 10.36945 11.18582 13 2013 9.878631 11.0726 14 2013 9.971707 11.05936 15 2013 10.0575 11.08756 16 2013 10.20233 11.14399 17 2013 10.14624 11.02723 18 2013 9.879041 10.97432 19 2013 10.00406 11.02241 20 2013 10.25857 11.1648 21 2013 10.42955 11.14669 22 2013 9.923535 11.04971 23 2013 10.04329 11.06462 24 2013 10.13321 11.11524 25 2013 10.39035 11.20462 26 2013 10.05617 11.14541 27 2013 10.06216 11.11842 28 2013 10.08168 11.21829 29 2013 10.19347 11.32347 30 2013 10.2509 11.29164 31 2013 10.021 11.26334 32 2013 10.04863 11.25205 33 2013 10.12415 11.26457 34 2013 10.42899 11.28143 35 2013 9.972874 11.12966 36 2013 9.811756 11.10172 37 2013 9.864227 11.10297 38 2013 10.0174 11.06164 39 2013 9.921278 11.02241 40 2013 9.79568 11.00686 41 2013 9.940253 11.05972 42 2013 10.05595 11.11215 43 2013 10.11131 11.04937 44 2013 9.603395 10.9504 45 2013 9.723523 11.00896 46 2013 9.863915 11.05559 47 2013 10.20814 11.08708 48 2013 9.721606 10.91369 49 2013 9.669788 10.92228 50 2013 9.873131 10.93519 51 2013 9.990811 11.00627 52 2013 9.651366 10.49255 0 2014 9.381432 10.50381 1 2014 9.392162 11.03046 2 2014 9.713174 11.07163 3 2014 9.809342 11.12891 4 2014 9.986955 11.10241 5 2014 9.488502 11.0561 6 2014 9.833226 11.19188 7 2014 10.16574 11.24522 8 2014 10.4304 11.23107 9 2014 9.709357 11.12648 10 2014 9.98815 11.13966 11 2014 10.07631 11.19915 12 2014 10.21841 11.26174 13 2014 10.09005 11.09156 14 2014 9.982576 11.03743 15 2014 10.05959 11.12678 16 2014 10.14471 11.16423 17 2014 10.2376 11.08667 18 2014 9.961143 11.03373 19 2014 10.05651 11.13076 20 2014 10.17329 11.17912 21 2014 10.3772 11.13428 22 2014 9.946595 11.02451 23 2014 10.01864 11.04596 24 2014 10.09448 11.07414 25 2014 10.27215 11.15696 26 2014 10.21365 11.11531 27 2014 10.19388 11.16723 28 2014 10.08997 11.1838 29 2014 10.05651 11.17566 30 2014 10.19096 11.1527 31 2014 9.795457 11.072 32 2014 9.921769 11.09712 33 2014 10.07786 11.21036 34 2014 10.41085 11.3168 35 2014 10.05199 11.20929 36 2014 9.903837 11.1398 37 2014 9.965711 11.15681 38 2014 10.09237 11.18805 39 2014 10.07933 11.15161 40 2014 9.766636 11.13017 41 2014 9.85692 11.15869 42 2014 9.906732 11.16284 43 2014 10.16908 11.17224 44 2014 9.640368 11.14215 45 2014 9.805875 11.21039 46 2014 9.870758 11.32554 47 2014 10.12579 11.27662 48 2014 9.86277 11.19193 49 2014 9.844268 11.13488 50 2014 10.06373 11.17643 51 2014 10.07643 11.15661 52 2014 10.1118 10.81565 ... View more