The followings are copied from the PROC ARIMA doc. You might see different formulations from other software vendors. We are not aware of any issue with PROC ARIMA having sign switched. thanks   General Notation for ARIMA Models Notation for Pure ARIMA Models Mathematically the pure ARIMA model is written as where t indexes time is the response series or a difference of the response series is the mean term B is the backshift operator; that is, is the autoregressive operator, represented as a polynomial in the backshift operator: is the moving-average operator, represented as a polynomial in the backshift operator: is the independent disturbance, also called the random error The series is computed by the IDENTIFY statement and is the series processed by the ESTIMATE statement. Thus, is either the response series Y or a difference of specified by the differencing operators in the IDENTIFY statement. For simple (nonseasonal) differencing, . For seasonal differencing , where d is the degree of nonseasonal differencing, D is the degree of seasonal differencing, and s is the length of the seasonal cycle. For example, the mathematical form of the ARIMA(1,1,1) model estimated in the preceding example is ... View more

How about PROC NLMIXED? alex ... View more

I am not familiar with the Hausman test either but i think you can use PROC MIXED (in SAS/STAT) to get all the param estimates to compute the Hausman test statistic. Hope this helps. ... View more

Here is the code to use PROC TIMEDATA to fill the time ID gaps using the previous values.   data test; length claim $3 claims_lodgement_date date_of_injury snapshot_date dev_month_lodge dev_month_injury netcostc netpayc 8; format claims_lodgement_date date_of_injury snapshot_date date9. netcostc netpayc dollar10. ; format dev_month_lodge dev_month_injury 3.; informat claims_lodgement_date date_of_injury snapshot_date date9.; infile datalines delimiter=',' dsd; input claim claims_lodgement_date date_of_injury snapshot_date dev_month_lodge dev_month_injury netcostc netpayc ; put _all_; datalines; XYZ,01FEB1994,21JUL1993,28FEB1994,0,7,80,80 XYZ,01FEB1994,21JUL1993,30APR1994,2,9,3961,3961 ; run; proc timedata data=test out=step11; by claim; var claims_lodgement_date date_of_injury Dev_Month_Lodge Dev_Month_Injury netcostc netpayc; id snapshot_date interval = month setmissing = prev; run; ... View more

Yes the suggestion was to formulate a GLM model with time series features such as lagged dependent variable and seasonal dummies (you can use the CLASS statement in GLM to create the dummies for you automatically). The model is not exactly the same as ARIMA but you can use this proxy model to select input variables, and then use the selected input variables in the subsequent time series models. thanks ... View more

One more note. If you take a look at the sas code generated by Forecast Studio (In the Forecast Studio project, go to project and select SAS Code...), you will see the following at the top of the code. Since the customized models are stored in the project level model repository, the project level repo has to be concatenated with the level specific repo to be used in the subsequent PROC calls. This feature is similar to concatenating SAS libnames.   *---------------------------------------------------------------------- * concatenate default hpf model repositry with the project. *---------------------------------------------------------------------; catname _project.ProjectModCombRep ( _project.ProjectModRep sashelp.hpfdflt (ACCESS=READONLY) ); catname _top.LevModRep( _top.AutoLevModRep _project.ProjectModCombRep ); catname _HPF0.LevModRep ( _HPF0.AutoLevModRep _project.ProjectModCombRep); catname _HPF1.LevModRep ( _HPF1.AutoLevModRep _project.ProjectModCombRep); catname _HPF2.LevModRep ( _HPF2.AutoLevModRep _project.ProjectModCombRep); ... View more

If you would like to see the model selection stats for all the models considered, you can go to the project folder (e.g. C:\SAS\Config\Lev1\AppData\SASForecastServer14.1\Projects\Pricedata), navigate down to \hierarchy\<level of interests> and take a look at OUTSTATSELECT table. The table contains all the stats you can image for all the candidate models considered for each series in that particular hierarchy level. The column _REGION_ could have values FIT and FORECAST. The row for FIT region is the in-sample stats and FORECAST region is for out-of-sample stats providing that the BACK option is set to something other than 0. I bet you can come up with lots of reports out of this single table.    It is a bit tricky to programmatically choose a model from the candidate lists (this is what forecast studio is for :>). The system generated model candidate lists (i.e. the model repository) are stored as a SAS catalog called AUTOLEVMODREP in the same folder as OUTSTATSELECT table. This catalog should contain the specifications for each individual candidate model and a selection specifications for the candidate models of each series. Any manual selection or custom models defined via forecast studio are stored in the project level model repository PROJECTMODREP in the project folder and the corresponding selection specifications will be removed from the AUTOLEVMODREP model repository.    Hope this helps a little. Alex ... View more