Dear Steve, I do have a problem with AR(1) while using Glimmix procedure. I have seen that you are very familiar with that command, so I decided to contact you directly. Your help would be more than important and valuable for me at this point. My problem is related to the Glimmix warning: obtaining minimum variance quadratic unbiased estimated as starting values for the covariance parameters failed. I have seen that you have addressed this question in the past  with the following command: random studyday /residual  type=&covtype subject= anml_nbr ;*group=grp_no; %if "&covtype" = "AR(1)" OR "&covtype" = "ARH(1)" %then %do; random intercept/subject=anml_nbr; %end; I have panel data with racially motivated crimes are my dependent variable. The data are structured as pooled cross-national time series, where time is nested . In other words, my dependent and independent variables were collected over time at the regional level. I have detected significant autocorrelation in my dependent variable. There is also an issue of heteroskedasticity. Here is the command I use: PROC GLIMMIX DATA=WORK.SAS NOLCPRINT; CLASS REGION_ID; MODEL=RACIST_CRIME IMMIGRATION GDP DENSITY TREND /SOLUTION DIST=POI LINK=LOG OFFSET=POPUL_LOG DDFM=BW; RANDOM INTERCEPT/SUB=REGION_ID TYPE =AR(1) RESIDUAL; RUN; I was told to use REPEATED instead of RANDOM, but the former seems to be appropriate only for proc mixed. Here instead I should use RANDOM ... RESIDUAL. I do not receive the  warning when I exclude RESIDUAL, but then I get 0 for the estimate for AR(1)  and missing value for its standard error. Thus, I do not think I control for autocorrletation in my data. When I include RANDOM  RESIDUAL, I get the warning: obtaining minimum variance...I have read that I should use AR(1) plus random effects. I have read that I should use AR(1) plus random effects. Could you please tell me how to incorporate that? Or perhaps I should use different TYPE. I would be extremely thankful for any help. Sincerely, Sylwia ... View more