For the sake of closure, I post the response I received from SAS technical support: William, As I suspected there is what we call "near" singularity in the data and model that tends to happen with polynomial models. The condition number of X`X matrix is huge as shown in the following PROC IML program: proc iml; use sasuser.co2; read all var {year}; x = j(nrow(year),1) || year || year##2; print (max(eigval(x`*x))/min(eigval(x`*x))); quit; 3.1771E21 In this case GLM and REG algorithm handles this particular near singularity better than GLMSELECT. The general recommendation is not use raw Year values in polynomial models; center and scale values of year first. For example, I center the polynomial term and now I reproduce the same results as REG and GLM. procmeansdata=sasuser.co2; varyear; run; datanew; setsasuser.co2; centeryr=year - 1988; run; procglmselectdata=new; modelco2= year centeryr*centeryr/selection=none; quit; I hope the above information is helpful to you. Please let me know if you have further questions on this particular matter. Thank you for using SAS and for your patience in my reply. Kathleen Kiernan Senior Principal Technical Support Statistician
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