About jhmoon

jhmoon · ‎01-03-2024

Thank you for your help, RIck. Then, I will contact Technical Support team, following your suggestion.

jhmoon · ‎12-27-2023

Thank you for replying and suggestion, Rick. Here's the result for your suggestions. 1. SAS version %put &=SYSVLONG; --> SYSVLONG=9.04.01M8P011823 2. To help us figure out the cause, can you please 1. Use a QUIT statement in the GLM step. 2. Modify the GLMSELECT step to explicitly specify the reference levels and the GLM parameterization. CLASS EVENT_TIME(REF='-1') AGE(REF='39') STD_YYYY(REF='2019') / param=GLM; Do you get the same parameter estimates? --> yes, it shows exactly same results. 3. "This could be a data-dependent problem caused by having the same (or very small) weights for the STD_YYYY variable. We should look at the distribution of weights for each level of the STD_YYYY variable. Please run the following and post the results." proc means DATA=IPW.TREAT_BASE_SAMPLE_CHT70 ; where not cmiss(Event_time, Age); class STD_YYYY; var C1_WEIGHT; run; proc sgplot DATA=IPW.TREAT_BASE_SAMPLE_CHT70 ; where not cmiss(Event_time, Age); vbox C1_WEIGHT / category= STD_YYYY; run; --> Here's my code and results below: /*RICK'S SOLUTION TO CHECK*/ PROC MEANS DATA=IPW.TREAT_BASE_SAMPLE_CHT70 ; WHERE NOT CMISS(EVENT_TIME, AGE); CLASS STD_YYYY; VAR C1_WEIGHT; RUN; ODS GRAPHICS ON / OBSMAX=5882292; PROC SGPLOT DATA=IPW.TREAT_BASE_SAMPLE_CHT70 ; WHERE NOT CMISS(EVENT_TIME, AGE); VBOX C1_WEIGHT / CATEGORY= STD_YYYY; RUN; In addition, I follow your suggestion with AGE instead of STD_YYYY You mentioned in last replying that it could be caused by having same weight. But, I am still wondering why there are different results between 'glm' and 'glmselect'. Even if it is a problem of weights, I think both codes should report the same results. Thank you

jhmoon · ‎12-21-2023

Sorry for late replying. I have to export the result file and it takes several days. I'll reply right after the file is exported.

jhmoon · ‎12-18-2023

It's SAS Enterprise Guide 7.15.

jhmoon · ‎12-18-2023

And I use SAS Enterprise Guide!

jhmoon · ‎12-18-2023

I posted again after revision. https://communities.sas.com/t5/Statistical-Procedures/problem-in-glm-and-glmselect-revised/m-p/908538#M45090 Thank you

jhmoon · ‎12-18-2023

Dear all, I posted same title 2 weeks ago, and I do it again after making it clear to see. previous post: https://communities.sas.com/t5/Statistical-Procedures/Different-results-between-glm-and-glmselect/td-p/905926 My problem is that when I did a event study analysis with code 'glm' and 'glmselect', the results are not the same, and I want to know why. Specifically, I use event-study method, and the outcome variable is "calc_cpi", RHS variables are 9 event time dummies, 18 year dummies and 18 age dummies. Weight variable here is constructed by this way: the number of cohort 2 / the number of cohort 1 (by age at event occurs). So, cohort 2 has 1 for the weight for all samples. Cohort 1 has the weight by age at event time zero. Case 1: all the results are right both by glm and glmselect without weights applied. Case 2: 'glmselect' has a problem. Here's the code and output. (first one is glm result, and second one is glmselect result) PROC GLM DATA=IPW.TREAT_BASE_SAMPLE_CHT70 ; CLASS EVENT_TIME(REF='-1') AGE(REF='39') STD_YYYY(REF='2019'); MODEL CALC_CPI = EVENT_TIME AGE STD_YYYY/ SOLUTION; WEIGHT C1_WEIGHT; OUTPUT OUT= PRED_CALC_CPI_WC1 PREDICTED=P RESIDUAL=R; ODS OUTPUT ParameterEstimates=PARAM_CALC_CPI_WC1; RUN; PROC GLMSELECT DATA=IPW.TREAT_BASE_SAMPLE_CHT70 ; CLASS EVENT_TIME(REF='-1') AGE STD_YYYY ; MODEL CALC_CPI = EVENT_TIME AGE STD_YYYY/ SELECTION=NONE ; WEIGHT C1_WEIGHT; OUTPUT OUT= PRED_CALC_CPI_WC1 PREDICTED=P RESIDUAL=R; ODS OUTPUT ParameterEstimates=PARAM_CALC_CPI_WC1; RUN; Dependent Parameter Estimate Biased StdErr tValue Probt CALC_CPI Intercept 57493.58084 1 598.5360913 96.06 <.0001 CALC_CPI EVENT_TIME -3 1151.43048 1 67.746409 17 <.0001 CALC_CPI EVENT_TIME -2 1290.48506 1 63.7409616 20.25 <.0001 CALC_CPI EVENT_TIME 0 -6709.90206 1 63.2582534 -106.07 <.0001 CALC_CPI EVENT_TIME 1 -16152.14249 1 65.8422001 -245.32 <.0001 CALC_CPI EVENT_TIME 2 -18193.03078 1 67.8515907 -268.13 <.0001 CALC_CPI EVENT_TIME 3 -19273.77713 1 70.6162547 -272.94 <.0001 CALC_CPI EVENT_TIME 4 -20520.90805 1 73.7837183 -278.12 <.0001 CALC_CPI EVENT_TIME 5 -21161.09299 1 77.6282235 -272.6 <.0001 CALC_CPI EVENT_TIME -1 0 1 CALC_CPI AGE 22 -33011.33226 1 453.9691843 -72.72 <.0001 CALC_CPI AGE 23 -31030.13856 1 417.0497166 -74.4 <.0001 CALC_CPI AGE 24 -28456.20384 1 404.3707065 -70.37 <.0001 CALC_CPI AGE 25 -26188.46721 1 398.0201918 -65.8 <.0001 CALC_CPI AGE 26 -24184.5052 1 394.2489703 -61.34 <.0001 CALC_CPI AGE 27 -22544.71582 1 391.5407826 -57.58 <.0001 CALC_CPI AGE 28 -20991.74813 1 389.4779456 -53.9 <.0001 CALC_CPI AGE 29 -19253.74307 1 387.720454 -49.66 <.0001 CALC_CPI AGE 30 -17464.85105 1 386.0938409 -45.23 <.0001 CALC_CPI AGE 31 -15496.64241 1 384.8382356 -40.27 <.0001 CALC_CPI AGE 32 -13496.68391 1 383.4668709 -35.2 <.0001 CALC_CPI AGE 33 -11624.09823 1 381.9453759 -30.43 <.0001 CALC_CPI AGE 34 -9663.06563 1 380.3173421 -25.41 <.0001 CALC_CPI AGE 35 -7642.13793 1 377.3506788 -20.25 <.0001 CALC_CPI AGE 36 -5740.0934 1 374.7912852 -15.32 <.0001 CALC_CPI AGE 37 -3900.21078 1 373.8001484 -10.43 <.0001 CALC_CPI AGE 38 -1949.43689 1 377.7374853 -5.16 <.0001 CALC_CPI AGE 39 0 1 CALC_CPI STD_YYYY 2002 -16249.47242 1 723.7739723 -22.45 <.0001 CALC_CPI STD_YYYY 2003 -13821.10908 1 715.5893296 -19.31 <.0001 CALC_CPI STD_YYYY 2004 -11888.8523 1 712.2320853 -16.69 <.0001 CALC_CPI STD_YYYY 2005 -11398.111 1 710.29501 -16.05 <.0001 CALC_CPI STD_YYYY 2006 -9609.67612 1 708.9337932 -13.56 <.0001 CALC_CPI STD_YYYY 2007 -7473.76668 1 707.8289777 -10.56 <.0001 CALC_CPI STD_YYYY 2008 -5956.85424 1 706.8830577 -8.43 <.0001 CALC_CPI STD_YYYY 2009 -5790.1094 1 706.0041004 -8.2 <.0001 CALC_CPI STD_YYYY 2010 -5811.82223 1 705.1423241 -8.24 <.0001 CALC_CPI STD_YYYY 2011 -5341.59143 1 704.5219159 -7.58 <.0001 CALC_CPI STD_YYYY 2012 -4584.69526 1 703.8424319 -6.51 <.0001 CALC_CPI STD_YYYY 2013 -4165.1198 1 703.2508081 -5.92 <.0001 CALC_CPI STD_YYYY 2014 -3306.6357 1 702.6738477 -4.71 <.0001 CALC_CPI STD_YYYY 2015 -2796.68722 1 700.2987691 -3.99 <.0001 CALC_CPI STD_YYYY 2016 -2641.5268 1 700.5926244 -3.77 0.0002 CALC_CPI STD_YYYY 2017 -2145.88724 1 704.8095795 -3.04 0.0023 CALC_CPI STD_YYYY 2018 -1733.12006 1 722.7610169 -2.4 0.0165 CALC_CPI STD_YYYY 2019 0 1 Effect EVENT_TIME AGE STD_YYYY Parameter DF Estimate StandardizedEst StdErr tValue Probt Intercept Intercept 1 5616.721735 0 5861231.563 0.00 0.9992 EVENT_TIME -3 EVENT_TIME -3 1 1151.430482 0.010214407 67.74641492 17.00 0.0000 EVENT_TIME -2 EVENT_TIME -2 1 1290.485072 0.011445652 63.74096714 20.25 0.0000 EVENT_TIME 0 EVENT_TIME 0 1 -6709.902053 -0.059401827 63.25825897 -106.07 0.0000 EVENT_TIME 1 EVENT_TIME 1 1 -16152.14248 -0.139525561 65.84220583 -245.32 0.0000 EVENT_TIME 2 EVENT_TIME 2 1 -18193.03077 -0.159262176 67.85159667 -268.13 0.0000 EVENT_TIME 3 EVENT_TIME 3 1 -19273.77712 -0.16878725 70.61626087 -272.94 0.0000 EVENT_TIME 4 EVENT_TIME 4 1 -20520.90803 -0.179322043 73.78372473 -278.12 0.0000 EVENT_TIME 5 EVENT_TIME 5 1 -21161.09296 -0.185167699 77.62823033 -272.60 0.0000 EVENT_TIME -1 EVENT_TIME -1 0 0 0 AGE 22 AGE 22 1 -33011.33219 -0.072258126 453.9692229 -72.72 0.0000 AGE 23 AGE 23 1 -31030.13848 -0.105624115 417.0497519 -74.40 0.0000 AGE 24 AGE 24 1 -28456.20376 -0.129083905 404.3707406 -70.37 0.0000 AGE 25 AGE 25 1 -26188.46713 -0.148142386 398.0202253 -65.80 0.0000 AGE 26 AGE 26 1 -24184.50513 -0.16058298 394.2490035 -61.34 0.0000 AGE 27 AGE 27 1 -22544.71575 -0.169706244 391.5408156 -57.58 0.0000 AGE 28 AGE 28 1 -20991.74806 -0.171072624 389.4779784 -53.90 0.0000 AGE 29 AGE 29 1 -19253.743 -0.164343939 387.7204866 -49.66 0.0000 AGE 30 AGE 30 1 -17464.85098 -0.152418958 386.0938734 -45.23 0.0000 AGE 31 AGE 31 1 -15496.64234 -0.132617654 384.8382679 -40.27 0.0000 AGE 32 AGE 32 1 -13496.68384 -0.11066162 383.4669031 -35.20 0.0000 AGE 33 AGE 33 1 -11624.09816 -0.089483332 381.9454081 -30.43 0.0000 AGE 34 AGE 34 1 -9663.065559 -0.067725564 380.3173741 -25.41 0.0000 AGE 35 AGE 35 1 -7642.137868 -0.046998557 377.3507106 -20.25 0.0000 AGE 36 AGE 36 1 -5740.093333 -0.028803712 374.7913168 -15.32 0.0000 AGE 37 AGE 37 1 -3900.210716 -0.01479889 373.8001801 -10.43 0.0000 AGE 38 AGE 38 1 -1949.436833 -0.005011604 377.7375174 -5.16 0.0000 AGE 39 AGE 39 0 0 0 STD_YYYY 2002 STD_YYYY 2002 1 35627.3866 0.142964368 5861231.573 0.01 0.9952 STD_YYYY 2003 STD_YYYY 2003 1 38055.74994 0.207465875 5861231.572 0.01 0.9948 STD_YYYY 2004 STD_YYYY 2004 1 39988.00673 0.262688821 5861231.571 0.01 0.9946 STD_YYYY 2005 STD_YYYY 2005 1 40478.74803 0.297424003 5861231.571 0.01 0.9945 STD_YYYY 2006 STD_YYYY 2006 1 42267.1829 0.333966531 5861231.571 0.01 0.9942 STD_YYYY 2007 STD_YYYY 2007 1 44403.09234 0.369594865 5861231.571 0.01 0.9940 STD_YYYY 2008 STD_YYYY 2008 1 45920.00478 0.393761508 5861231.571 0.01 0.9937 STD_YYYY 2009 STD_YYYY 2009 1 46086.74962 0.401552632 5861231.571 0.01 0.9937 STD_YYYY 2010 STD_YYYY 2010 1 46065.03678 0.402040126 5861231.571 0.01 0.9937 STD_YYYY 2011 STD_YYYY 2011 1 46535.26758 0.370095463 5861231.571 0.01 0.9937 STD_YYYY 2012 STD_YYYY 2012 1 47292.16376 0.338098445 5861231.571 0.01 0.9936 STD_YYYY 2013 STD_YYYY 2013 1 47711.73922 0.296078403 5861231.571 0.01 0.9935 STD_YYYY 2014 STD_YYYY 2014 1 48570.22332 0.256641829 5861231.571 0.01 0.9934 STD_YYYY 2015 STD_YYYY 2015 1 49080.1718 0.212832555 5861231.571 0.01 0.9933 STD_YYYY 2016 STD_YYYY 2016 1 49235.33223 0.162905538 5861231.571 0.01 0.9933 STD_YYYY 2017 STD_YYYY 2017 1 49730.97179 0.116719528 5861231.572 0.01 0.9932 STD_YYYY 2018 STD_YYYY 2018 1 50143.73898 0.071874329 5861231.575 0.01 0.9932 STD_YYYY 2019 STD_YYYY 2019 1 51876.85768 0.035889768 5861231.593 0.01 0.9929 With glmselect code, STD_YYYY has no reference dummy which should be omitted. Case 3: When I do a subgroup analysis with 'by' option, there's a different problem here. 'Metro' variable is a dummy for indicating the metro city. PROC GLM DATA=IPW.TREAT_BASE_SAMPLE_CHT70 ; CLASS EVENT_TIME(REF='-1') AGE(REF='39') STD_YYYY(REF='2019'); MODEL CALC_CPI = EVENT_TIME AGE STD_YYYY/ SOLUTION; BY METRO; WEIGHT C1_WEIGHT; OUTPUT OUT= PRED_CALC_CPI_WC1 PREDICTED=P RESIDUAL=R; ODS OUTPUT ParameterEstimates=PARAM_CALC_CPI_WC1; RUN; PROC GLMSELECT DATA=IPW.TREAT_BASE_SAMPLE_CHT70 ; CLASS EVENT_TIME(REF='-1') AGE STD_YYYY ; MODEL CALC_CPI = EVENT_TIME AGE STD_YYYY/ SELECTION=NONE ; BY METRO; WEIGHT C1_WEIGHT; OUTPUT OUT= PRED_CALC_CPI_WC1 PREDICTED=P RESIDUAL=R; ODS OUTPUT ParameterEstimates=PARAM_CALC_CPI_WC1; RUN; METRO Dependent Parameter Estimate Biased StdErr tValue Probt 0 CALC_CPI Intercept -3895.12278 1 7729975.654 0.00 0.9996 0 CALC_CPI EVENT_TIME -3 978.61218 1 87.79501768 11.15 0.0000 0 CALC_CPI EVENT_TIME -2 1180.69630 1 82.27551899 14.35 0.0000 0 CALC_CPI EVENT_TIME 0 -6342.50198 1 81.54173762 -77.78 0.0000 0 CALC_CPI EVENT_TIME 1 -14450.89523 1 84.74718996 -170.52 0.0000 0 CALC_CPI EVENT_TIME 2 -16030.37882 1 87.51248946 -183.18 0.0000 0 CALC_CPI EVENT_TIME 3 -16749.01300 1 91.07836901 -183.90 0.0000 0 CALC_CPI EVENT_TIME 4 -17470.41779 1 95.08922474 -183.73 0.0000 0 CALC_CPI EVENT_TIME 5 -17515.59945 1 99.91195499 -175.31 0.0000 0 CALC_CPI EVENT_TIME -1 0.00000 1 0 CALC_CPI AGE 22 3726.46625 0 16834818.1 0.00 0.9998 0 CALC_CPI AGE 23 5682.55616 0 16834818.1 0.00 0.9997 0 CALC_CPI AGE 24 8018.60850 0 16834818.1 0.00 0.9996 0 CALC_CPI AGE 25 9747.53333 0 16834818.1 0.00 0.9995 0 CALC_CPI AGE 26 11121.30219 0 16834818.1 0.00 0.9995 0 CALC_CPI AGE 27 12108.09981 0 16834818.1 0.00 0.9994 0 CALC_CPI AGE 28 13026.26507 0 16834818.1 0.00 0.9994 0 CALC_CPI AGE 29 14138.23528 0 16834818.1 0.00 0.9993 0 CALC_CPI AGE 30 15416.94606 0 16834818.1 0.00 0.9993 0 CALC_CPI AGE 31 16866.27534 0 16834818.1 0.00 0.9992 0 CALC_CPI AGE 32 18346.09867 0 16834818.1 0.00 0.9991 0 CALC_CPI AGE 33 19629.98543 0 16834818.1 0.00 0.9991 0 CALC_CPI AGE 34 20940.93670 0 16834818.1 0.00 0.9990 0 CALC_CPI AGE 35 22268.69419 0 16834818.1 0.00 0.9989 0 CALC_CPI AGE 36 23498.42169 0 16834818.1 0.00 0.9989 0 CALC_CPI AGE 37 24639.75805 0 16834818.1 0.00 0.9988 0 CALC_CPI AGE 38 26288.30791 0 16834818.1 0.00 0.9988 0 CALC_CPI AGE 39 27677.95218 0 16834818.11 0.00 0.9987 0 CALC_CPI STD_YYYY 2002 8299.40414 0 16722040.85 0.00 0.9996 0 CALC_CPI STD_YYYY 2003 10663.93296 0 16722040.85 0.00 0.9995 0 CALC_CPI STD_YYYY 2004 12542.76508 0 16722040.85 0.00 0.9994 0 CALC_CPI STD_YYYY 2005 13203.85921 0 16722040.85 0.00 0.9994 0 CALC_CPI STD_YYYY 2006 14851.59210 0 16722040.85 0.00 0.9993 0 CALC_CPI STD_YYYY 2007 16799.61320 0 16722040.85 0.00 0.9992 0 CALC_CPI STD_YYYY 2008 18178.73109 0 16722040.85 0.00 0.9991 0 CALC_CPI STD_YYYY 2009 18532.44842 0 16722040.85 0.00 0.9991 0 CALC_CPI STD_YYYY 2010 18300.38044 0 16722040.85 0.00 0.9991 0 CALC_CPI STD_YYYY 2011 18846.57380 0 16722040.85 0.00 0.9991 0 CALC_CPI STD_YYYY 2012 19583.46699 0 16722040.85 0.00 0.9991 0 CALC_CPI STD_YYYY 2013 20241.00004 0 16722040.85 0.00 0.9990 0 CALC_CPI STD_YYYY 2014 21323.54965 0 16722040.85 0.00 0.9990 0 CALC_CPI STD_YYYY 2015 21817.24317 0 16722040.85 0.00 0.9990 0 CALC_CPI STD_YYYY 2016 22159.80750 0 16722040.85 0.00 0.9989 0 CALC_CPI STD_YYYY 2017 22649.12430 0 16722040.86 0.00 0.9989 0 CALC_CPI STD_YYYY 2018 23302.41868 0 16722040.86 0.00 0.9989 0 CALC_CPI STD_YYYY 2019 24949.93483 0 16722040.88 0.00 0.9988 1 CALC_CPI Intercept 42176.43379 1 8924444.166 0.00 0.9962 1 CALC_CPI EVENT_TIME -3 994.34186 1 101.1486969 9.83 0.0000 1 CALC_CPI EVENT_TIME -2 1215.89427 1 95.45162941 12.74 0.0000 1 CALC_CPI EVENT_TIME 0 -6843.48301 1 94.85321721 -72.15 0.0000 1 CALC_CPI EVENT_TIME 1 -17267.81526 1 98.94975143 -174.51 0.0000 1 CALC_CPI EVENT_TIME 2 -19545.15122 1 101.8492618 -191.90 0.0000 1 CALC_CPI EVENT_TIME 3 -20748.45919 1 106.0721071 -195.61 0.0000 1 CALC_CPI EVENT_TIME 4 -22258.63616 1 110.9774742 -200.57 0.0000 1 CALC_CPI EVENT_TIME 5 -23210.70218 1 116.9501239 -198.47 0.0000 1 CALC_CPI EVENT_TIME -1 0.00000 1 1 CALC_CPI AGE 22 -16614.47689 1 8924444.227 0.00 0.9985 1 CALC_CPI AGE 23 -14644.87904 1 8924444.222 0.00 0.9987 1 CALC_CPI AGE 24 -11836.91128 1 8924444.221 0.00 0.9989 1 CALC_CPI AGE 25 -9061.92505 1 8924444.22 0.00 0.9992 1 CALC_CPI AGE 26 -6561.32080 1 8924444.22 0.00 0.9994 1 CALC_CPI AGE 27 -4455.03090 1 8924444.219 0.00 0.9996 1 CALC_CPI AGE 28 -2508.50369 1 8924444.219 0.00 0.9998 1 CALC_CPI AGE 29 -433.24748 1 8924444.219 0.00 1.0000 1 CALC_CPI AGE 30 1583.46575 1 8924444.219 0.00 0.9999 1 CALC_CPI AGE 31 3797.79359 1 8924444.219 0.00 0.9997 1 CALC_CPI AGE 32 6034.52033 1 8924444.219 0.00 0.9995 1 CALC_CPI AGE 33 8181.42850 1 8924444.219 0.00 0.9993 1 CALC_CPI AGE 34 10412.49360 1 8924444.219 0.00 0.9991 1 CALC_CPI AGE 35 12734.45466 1 8924444.218 0.00 0.9989 1 CALC_CPI AGE 36 14889.39073 1 8924444.218 0.00 0.9987 1 CALC_CPI AGE 37 17002.81232 1 8924444.218 0.00 0.9985 1 CALC_CPI AGE 38 18969.41217 1 8924444.219 0.00 0.9983 1 CALC_CPI AGE 39 21104.02156 1 8924444.204 0.00 0.9981 1 CALC_CPI STD_YYYY 2002 -16791.17764 1 1029.07981 -16.32 0.0000 1 CALC_CPI STD_YYYY 2003 -14332.50773 1 1015.028264 -14.12 0.0000 1 CALC_CPI STD_YYYY 2004 -12365.53006 1 1009.390721 -12.25 0.0000 1 CALC_CPI STD_YYYY 2005 -12066.34541 1 1006.206658 -11.99 0.0000 1 CALC_CPI STD_YYYY 2006 -10180.47215 1 1004.022003 -10.14 0.0000 1 CALC_CPI STD_YYYY 2007 -7857.90199 1 1002.274509 -7.84 0.0000 1 CALC_CPI STD_YYYY 2008 -6173.65299 1 1000.796782 -6.17 0.0000 1 CALC_CPI STD_YYYY 2009 -6142.29846 1 999.4356916 -6.15 0.0000 1 CALC_CPI STD_YYYY 2010 -5945.25020 1 998.1000149 -5.96 0.0000 1 CALC_CPI STD_YYYY 2011 -5474.52696 1 997.101357 -5.49 0.0000 1 CALC_CPI STD_YYYY 2012 -4632.71579 1 996.0329327 -4.65 0.0000 1 CALC_CPI STD_YYYY 2013 -4340.17720 1 995.1040046 -4.36 0.0000 1 CALC_CPI STD_YYYY 2014 -3545.84023 1 994.2626587 -3.57 0.0004 1 CALC_CPI STD_YYYY 2015 -2930.66837 1 990.6913263 -2.96 0.0031 1 CALC_CPI STD_YYYY 2016 -2804.04633 1 991.2891071 -2.83 0.0047 1 CALC_CPI STD_YYYY 2017 -2246.37908 1 997.3882092 -2.25 0.0243 1 CALC_CPI STD_YYYY 2018 -1946.53825 1 1022.994013 -1.90 0.0571 1 CALC_CPI STD_YYYY 2019 0.00000 1 METRO Effect EVENT_TIME AGE STD_YYYY Parameter DF Estimate StandardizedEst StdErr tValue Probt 0 Intercept Intercept 1 48732.76449 0 823.2806409 59.19 0.0000 0 EVENT_TIME -3 EVENT_TIME -3 1 978.6121821 0.009879003 87.79498495 11.15 0.0000 0 EVENT_TIME -2 EVENT_TIME -2 1 1180.696301 0.011916745 82.27548832 14.35 0.0000 0 EVENT_TIME 0 EVENT_TIME 0 1 -6342.501979 -0.063938752 81.54170722 -77.78 0.0000 0 EVENT_TIME 1 EVENT_TIME 1 1 -14450.89523 -0.142883041 84.74715836 -170.52 0.0000 0 EVENT_TIME 2 EVENT_TIME 2 1 -16030.37882 -0.160069536 87.51245683 -183.18 0.0000 0 EVENT_TIME 3 EVENT_TIME 3 1 -16749.013 -0.167109196 91.07833505 -183.90 0.0000 0 EVENT_TIME 4 EVENT_TIME 4 1 -17470.41779 -0.17395332 95.08918929 -183.73 0.0000 0 EVENT_TIME 5 EVENT_TIME 5 1 -17515.59946 -0.174604706 99.91191774 -175.31 0.0000 0 EVENT_TIME -1 EVENT_TIME -1 0 0 0 0 AGE 22 AGE 22 1 -23951.48593 -0.065256495 609.8976565 -39.27 0.0000 0 AGE 23 AGE 23 1 -21995.39602 -0.092291837 571.3578958 -38.50 0.0000 0 AGE 24 AGE 24 1 -19659.34367 -0.10864938 558.0981989 -35.23 0.0000 0 AGE 25 AGE 25 1 -17930.41885 -0.121767376 551.3697335 -32.52 0.0000 0 AGE 26 AGE 26 1 -16556.64999 -0.129777789 547.285133 -30.25 0.0000 0 AGE 27 AGE 27 1 -15569.85237 -0.136392915 544.2738391 -28.61 0.0000 0 AGE 28 AGE 28 1 -14651.6871 -0.137586654 541.9167938 -27.04 0.0000 0 AGE 29 AGE 29 1 -13539.7169 -0.132324866 539.8663674 -25.08 0.0000 0 AGE 30 AGE 30 1 -12261.00612 -0.122064747 537.9408 -22.79 0.0000 0 AGE 31 AGE 31 1 -10811.67684 -0.104877075 536.4959055 -20.15 0.0000 0 AGE 32 AGE 32 1 -9331.853508 -0.086131356 534.890358 -17.45 0.0000 0 AGE 33 AGE 33 1 -8047.966748 -0.069081574 533.0982466 -15.10 0.0000 0 AGE 34 AGE 34 1 -6737.015473 -0.051968364 531.201936 -12.68 0.0000 0 AGE 35 AGE 35 1 -5409.257986 -0.03622049 527.5083801 -10.25 0.0000 0 AGE 36 AGE 36 1 -4179.530482 -0.022558388 524.2580505 -7.97 0.0000 0 AGE 37 AGE 37 1 -3038.194123 -0.012260304 523.1339909 -5.81 0.0000 0 AGE 38 AGE 38 1 -1389.644267 -0.003768741 528.8018235 -2.63 0.0086 0 AGE 39 AGE 39 0 0 0 0 STD_YYYY 2002 STD_YYYY 2002 1 -16650.53095 -0.080354869 996.552424 -16.71 0.0000 0 STD_YYYY 2003 STD_YYYY 2003 1 -14286.00212 -0.092404093 987.432746 -14.47 0.0000 0 STD_YYYY 2004 STD_YYYY 2004 1 -12407.17001 -0.095531127 983.6120877 -12.61 0.0000 0 STD_YYYY 2005 STD_YYYY 2005 1 -11746.07587 -0.100170107 981.3609237 -11.97 0.0000 0 STD_YYYY 2006 STD_YYYY 2006 1 -10098.34299 -0.091994308 979.7390713 -10.31 0.0000 0 STD_YYYY 2007 STD_YYYY 2007 1 -8150.321888 -0.077817622 978.4025271 -8.33 0.0000 0 STD_YYYY 2008 STD_YYYY 2008 1 -6771.204 -0.06640663 977.2427986 -6.93 0.0000 0 STD_YYYY 2009 STD_YYYY 2009 1 -6417.486667 -0.063835716 976.1549733 -6.57 0.0000 0 STD_YYYY 2010 STD_YYYY 2010 1 -6649.554649 -0.066117202 975.0897738 -6.82 0.0000 0 STD_YYYY 2011 STD_YYYY 2011 1 -6103.361286 -0.054600383 974.3559472 -6.26 0.0000 0 STD_YYYY 2012 STD_YYYY 2012 1 -5366.468102 -0.042711614 973.5318086 -5.51 0.0000 0 STD_YYYY 2013 STD_YYYY 2013 1 -4708.935048 -0.032176676 972.8157802 -4.84 0.0000 0 STD_YYYY 2014 STD_YYYY 2014 1 -3626.385435 -0.020955799 972.0437018 -3.73 0.0002 0 STD_YYYY 2015 STD_YYYY 2015 1 -3132.691922 -0.014746356 969.0205189 -3.23 0.0012 0 STD_YYYY 2016 STD_YYYY 2016 1 -2790.127585 -0.00996555 969.1815037 -2.88 0.0040 0 STD_YYYY 2017 STD_YYYY 2017 1 -2300.810792 -0.00577359 974.814409 -2.36 0.0183 0 STD_YYYY 2018 STD_YYYY 2018 1 -1647.516408 -0.002507904 999.3242518 -1.65 0.0992 0 STD_YYYY 2019 STD_YYYY 2019 0 0 0 1 Intercept Intercept 1 63280.45558 0 850.487093 74.40 0.0000 1 EVENT_TIME -3 EVENT_TIME -3 1 994.3418626 0.008055379 101.1486802 9.83 0.0000 1 EVENT_TIME -2 EVENT_TIME -2 1 1215.894272 0.009848127 95.45161366 12.74 0.0000 1 EVENT_TIME 0 EVENT_TIME 0 1 -6843.483011 -0.055291696 94.85320156 -72.15 0.0000 1 EVENT_TIME 1 EVENT_TIME 1 1 -17267.81526 -0.135461874 98.94973511 -174.51 0.0000 1 EVENT_TIME 2 EVENT_TIME 2 1 -19545.15122 -0.155900401 101.849245 -191.90 0.0000 1 EVENT_TIME 3 EVENT_TIME 3 1 -20748.45919 -0.165749227 106.0720896 -195.61 0.0000 1 EVENT_TIME 4 EVENT_TIME 4 1 -22258.63616 -0.177409728 110.9774559 -200.57 0.0000 1 EVENT_TIME 5 EVENT_TIME 5 1 -23210.70219 -0.185285274 116.9501046 -198.47 0.0000 1 EVENT_TIME -1 EVENT_TIME -1 0 0 0 1 AGE 22 AGE 22 1 -37718.49845 -0.068051731 668.144711 -56.45 0.0000 1 AGE 23 AGE 23 1 -35748.9006 -0.101599922 599.7267032 -59.61 0.0000 1 AGE 24 AGE 24 1 -32940.93284 -0.126637202 576.2575101 -57.16 0.0000 1 AGE 25 AGE 25 1 -30165.94661 -0.147222185 564.6762622 -53.42 0.0000 1 AGE 26 AGE 26 1 -27665.34236 -0.16156506 557.9669949 -49.58 0.0000 1 AGE 27 AGE 27 1 -25559.05246 -0.171838212 553.2894143 -46.19 0.0000 1 AGE 28 AGE 28 1 -23612.52525 -0.173613597 549.8253636 -42.95 0.0000 1 AGE 29 AGE 29 1 -21537.26904 -0.166900789 546.9335527 -39.38 0.0000 1 AGE 30 AGE 30 1 -19520.55581 -0.155215734 544.2995877 -35.86 0.0000 1 AGE 31 AGE 31 1 -17306.22797 -0.135745804 542.2007501 -31.92 0.0000 1 AGE 32 AGE 32 1 -15069.50123 -0.113959816 539.9454294 -27.91 0.0000 1 AGE 33 AGE 33 1 -12922.59306 -0.092519626 537.4588657 -24.04 0.0000 1 AGE 34 AGE 34 1 -10691.52796 -0.07045734 534.77624 -19.99 0.0000 1 AGE 35 AGE 35 1 -8369.566899 -0.048821541 530.1984657 -15.79 0.0000 1 AGE 36 AGE 36 1 -6214.63083 -0.029862688 526.2921207 -11.81 0.0000 1 AGE 37 AGE 37 1 -4101.209234 -0.015029863 524.639301 -7.82 0.0000 1 AGE 38 AGE 38 1 -2134.609388 -0.005332029 529.9816263 -4.03 0.0001 1 AGE 39 AGE 39 0 0 0 1 STD_YYYY 2002 STD_YYYY 2002 1 -16791.17786 -0.058028694 1029.079642 -16.32 0.0000 1 STD_YYYY 2003 STD_YYYY 2003 1 -14332.50796 -0.068346207 1015.028098 -14.12 0.0000 1 STD_YYYY 2004 STD_YYYY 2004 1 -12365.53029 -0.072008031 1009.390556 -12.25 0.0000 1 STD_YYYY 2005 STD_YYYY 2005 1 -12066.34564 -0.07940633 1006.206493 -11.99 0.0000 1 STD_YYYY 2006 STD_YYYY 2006 1 -10180.47238 -0.072531178 1004.021839 -10.14 0.0000 1 STD_YYYY 2007 STD_YYYY 2007 1 -7857.902214 -0.059275162 1002.274346 -7.84 0.0000 1 STD_YYYY 2008 STD_YYYY 2008 1 -6173.653216 -0.048113405 1000.796619 -6.17 0.0000 1 STD_YYYY 2009 STD_YYYY 2009 1 -6142.298686 -0.048723706 999.4355283 -6.15 0.0000 1 STD_YYYY 2010 STD_YYYY 2010 1 -5945.250427 -0.047334151 998.0998518 -5.96 0.0000 1 STD_YYYY 2011 STD_YYYY 2011 1 -5474.527188 -0.040184299 997.1011941 -5.49 0.0000 1 STD_YYYY 2012 STD_YYYY 2012 1 -4632.716016 -0.030845737 996.0327699 -4.65 0.0000 1 STD_YYYY 2013 STD_YYYY 2013 1 -4340.177422 -0.025316809 995.103842 -4.36 0.0000 1 STD_YYYY 2014 STD_YYYY 2014 1 -3545.840451 -0.017710699 994.2624963 -3.57 0.0004 1 STD_YYYY 2015 STD_YYYY 2015 1 -2930.668589 -0.012085427 990.6911644 -2.96 0.0031 1 STD_YYYY 2016 STD_YYYY 2016 1 -2804.046552 -0.008861841 991.2889451 -2.83 0.0047 1 STD_YYYY 2017 STD_YYYY 2017 1 -2246.379304 -0.005073097 997.3880462 -2.25 0.0243 1 STD_YYYY 2018 STD_YYYY 2018 1 -1946.538475 -0.002698223 1022.993846 -1.90 0.0571 1 STD_YYYY 2019 STD_YYYY 2019 0 0 0 Now, there's a problem in glm results. I have no idea why this problem happened. Can you give any information or suggestion? Or is there any pages that I can see each steps of how weights are applied in the regression?

jhmoon · ‎12-06-2023

Thank you Rick, I will upload the post soon after I make it clear to see.

jhmoon · ‎12-05-2023

Thank you for help! I followed your second recommendation first, and I confirmed that there are no negative weights or missing value affecting the results. And below, I upload the results for glm and glmselect, respectively. The GLM Procedure Class Level Information Class Levels Values EVENT_TIME 9 -3 -2 0 1 2 3 4 5 -1 AGE 18 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 STD_YYYY 18 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Number of Observations Read 5882292 Number of Observations Used 5713157 The GLM Procedure Dependent Variable: EARNINGS Weight: C1_WEIGHT Source DF Sum of Squares Mean Square F Value Pr > F Model 42 2.40E+14 5.71E+12 5122.4 <.0001 Error 5.71E+06 6.37E+15 1.115E+09 　　 Corrected Total 5.71E+06 6.61E+15 　　　 R-Square Coeff Var Root MSE EARNINGS Mean 0.036291 147.9133 33394.8 22577.28 Source DF Type I SS Mean Square F Value Pr > F EVENT_TIME 8 5.76E+13 7.20E+12 6459.7 <.0001 AGE 17 1.73E+14 1.02E+13 9143.87 <.0001 STD_YYYY 17 8.94E+12 5.26E+11 471.62 <.0001 Source DF Type III SS Mean Square F Value Pr > F EVENT_TIME 8 1.90E+14 2.38E+13 21351.3 <.0001 AGE 17 3.09E+13 1.82E+12 1632.25 <.0001 STD_YYYY 17 8.94E+12 5.26E+11 471.62 <.0001 Parameter Estimate 　 Standard t Value Pr > |t| 95% Confidence Limits Expected Value Error Intercept 57493.581 B 598.53609 96.06 <.0001 56320.471 58666.69 Intercept + [EVENT_TIME -1] + [AGE 39] + [STD_YYYY 2019] EVENT_TIME -3 1151.4305 B 67.746409 17 <.0001 1018.6499 1284.211 [EVENT_TIME -3] - [EVENT_TIME -1] EVENT_TIME -2 1290.4851 B 63.740962 20.25 <.0001 1165.5551 1415.4151 [EVENT_TIME -2] - [EVENT_TIME -1] EVENT_TIME 0 -6709.9021 B 63.258253 -106.07 <.0001 -6833.886 -6585.9181 [EVENT_TIME 0] - [EVENT_TIME -1] EVENT_TIME 1 -16152.142 B 65.8422 -245.32 <.0001 -16281.191 -16023.094 [EVENT_TIME 1] - [EVENT_TIME -1] EVENT_TIME 2 -18193.031 B 67.851591 -268.13 <.0001 -18326.017 -18060.044 [EVENT_TIME 2] - [EVENT_TIME -1] EVENT_TIME 3 -19273.777 B 70.616255 -272.94 <.0001 -19412.182 -19135.372 [EVENT_TIME 3] - [EVENT_TIME -1] EVENT_TIME 4 -20520.908 B 73.783718 -278.12 <.0001 -20665.522 -20376.295 [EVENT_TIME 4] - [EVENT_TIME -1] EVENT_TIME 5 -21161.093 B 77.628224 -272.6 <.0001 -21313.242 -21008.944 [EVENT_TIME 5] - [EVENT_TIME -1] EVENT_TIME -1 0 B . . . . . 　 AGE 22 -33011.332 B 453.96918 -72.72 <.0001 -33901.096 -32121.569 [AGE 22] - [AGE 39] AGE 23 -31030.139 B 417.04972 -74.4 <.0001 -31847.541 -30212.736 [AGE 23] - [AGE 39] AGE 24 -28456.204 B 404.37071 -70.37 <.0001 -29248.756 -27663.652 [AGE 24] - [AGE 39] AGE 25 -26188.467 B 398.02019 -65.8 <.0001 -26968.573 -25408.362 [AGE 25] - [AGE 39] AGE 26 -24184.505 B 394.24897 -61.34 <.0001 -24957.219 -23411.791 [AGE 26] - [AGE 39] AGE 27 -22544.716 B 391.54078 -57.58 <.0001 -23312.122 -21777.31 [AGE 27] - [AGE 39] AGE 28 -20991.748 B 389.47795 -53.9 <.0001 -21755.111 -20228.385 [AGE 28] - [AGE 39] AGE 29 -19253.743 B 387.72045 -49.66 <.0001 -20013.661 -18493.825 [AGE 29] - [AGE 39] AGE 30 -17464.851 B 386.09384 -45.23 <.0001 -18221.581 -16708.121 [AGE 30] - [AGE 39] AGE 31 -15496.642 B 384.83824 -40.27 <.0001 -16250.912 -14742.373 [AGE 31] - [AGE 39] AGE 32 -13496.684 B 383.46687 -35.2 <.0001 -14248.265 -12745.103 [AGE 32] - [AGE 39] AGE 33 -11624.098 B 381.94538 -30.43 <.0001 -12372.698 -10875.499 [AGE 33] - [AGE 39] AGE 34 -9663.0656 B 380.31734 -25.41 <.0001 -10408.474 -8917.6572 [AGE 34] - [AGE 39] AGE 35 -7642.1379 B 377.35068 -20.25 <.0001 -8381.7318 -6902.544 [AGE 35] - [AGE 39] AGE 36 -5740.0934 B 374.79129 -15.32 <.0001 -6474.671 -5005.5158 [AGE 36] - [AGE 39] AGE 37 -3900.2108 B 373.80015 -10.43 <.0001 -4632.8458 -3167.5758 [AGE 37] - [AGE 39] AGE 38 -1949.4369 B 377.73749 -5.16 <.0001 -2689.7889 -1209.0849 [AGE 38] - [AGE 39] AGE 39 0 B . . . . . 　 STD_YYYY 2002 -16249.473 B 723.77397 -22.45 <.0001 -17668.044 -14830.901 [STD_YYYY 2002] - [STD_YYYY 2019] STD_YYYY 2003 -13821.109 B 715.58933 -19.31 <.0001 -15223.639 -12418.58 [STD_YYYY 2003] - [STD_YYYY 2019] STD_YYYY 2004 -11888.852 B 712.23209 -16.69 <.0001 -13284.802 -10492.903 [STD_YYYY 2004] - [STD_YYYY 2019] STD_YYYY 2005 -11398.111 B 710.29501 -16.05 <.0001 -12790.264 -10005.958 [STD_YYYY 2005] - [STD_YYYY 2019] STD_YYYY 2006 -9609.6762 B 708.93379 -13.56 <.0001 -10999.161 -8220.1912 [STD_YYYY 2006] - [STD_YYYY 2019] STD_YYYY 2007 -7473.7668 B 707.82898 -10.56 <.0001 -8861.0864 -6086.4472 [STD_YYYY 2007] - [STD_YYYY 2019] STD_YYYY 2008 -5956.8543 B 706.88306 -8.43 <.0001 -7342.32 -4571.3887 [STD_YYYY 2008] - [STD_YYYY 2019] STD_YYYY 2009 -5790.1095 B 706.0041 -8.2 <.0001 -7173.8524 -4406.3666 [STD_YYYY 2009] - [STD_YYYY 2019] STD_YYYY 2010 -5811.8223 B 705.14232 -8.24 <.0001 -7193.8762 -4429.7685 [STD_YYYY 2010] - [STD_YYYY 2019] STD_YYYY 2011 -5341.5915 B 704.52192 -7.58 <.0001 -6722.4294 -3960.7536 [STD_YYYY 2011] - [STD_YYYY 2019] STD_YYYY 2012 -4584.6953 B 703.84243 -6.51 <.0001 -5964.2015 -3205.1892 [STD_YYYY 2012] - [STD_YYYY 2019] STD_YYYY 2013 -4165.1199 B 703.25081 -5.92 <.0001 -5543.4664 -2786.7733 [STD_YYYY 2013] - [STD_YYYY 2019] STD_YYYY 2014 -3306.6358 B 702.67385 -4.71 <.0001 -4683.8515 -1929.4201 [STD_YYYY 2014] - [STD_YYYY 2019] STD_YYYY 2015 -2796.6873 B 700.29877 -3.99 <.0001 -4169.248 -1424.1267 [STD_YYYY 2015] - [STD_YYYY 2019] STD_YYYY 2016 -2641.5269 B 700.59262 -3.77 0.0002 -4014.6635 -1268.3903 [STD_YYYY 2016] - [STD_YYYY 2019] STD_YYYY 2017 -2145.8873 B 704.80958 -3.04 0.0023 -3527.289 -764.48564 [STD_YYYY 2017] - [STD_YYYY 2019] STD_YYYY 2018 -1733.1201 B 722.76102 -2.4 0.0165 -3149.706 -316.53428 [STD_YYYY 2018] - [STD_YYYY 2019] STD_YYYY 2019 0 B . . . . . 　 Note: The X'X matrix has been found to be singular, and a generalized inverse was used to solve the normal equations. Terms whose estimates are followed by the letter 'B' are not uniquely estimable. The GLMSELECT Procedure Data Set SAMPLE_CHT70 Weight C1_WEIGHT Dependent Variable EARNINGS Selection Method None Number of Observations Read 5882292 Number of Observations Used 5713157 Class Level Information Class Levels Values EVENT_TIME 9 -3 -2 0 1 2 3 4 5 -1 AGE 18 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 STD_YYYY 18 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Dimensions 　 Number of Effects 4 Number of Parameters 46 The GLMSELECT Procedure Least Squares Summary 　　　　 Step Effect Number Number SBC 　 Entered Effects In Parms In 　 0 Intercept 1 1 119229427 1 EVENT_TIME 2 9 119179530 2 AGE 3 26 119026637 3 STD_YYYY 4 44 119018905* * Optimal Value of Criterion 　　　　　 The GLMSELECT Procedure Least Squares Model (No Selection) Analysis of Variance Source DF Sum of Mean F Value Pr > F Model 43 Squares Square 　　 Error 5.71E+06 2.40E+14 5.58E+12 5003.28 <.0001 Corrected Total 5.71E+06 6.37E+15 1.115E+09 　　 Root MSE 33395 6.61E+15 　　　 Dependent Mean 22577 R-Square 0.0363 Adj R-Sq 0.0363 AIC 124731467 AICC 124731467 SBC 119018905 Parameter Estimates 　　 Standard t Value Pr > |t| Parameter DF Estimate Error 　　 Intercept 1 5106.229845 5578113 0 0.9993 EVENT_TIME -3 1 1151.430482 67.746415 17 <.0001 EVENT_TIME -2 1 1290.485072 63.740967 20.25 <.0001 EVENT_TIME 0 1 -6709.902053 63.258259 -106.07 <.0001 EVENT_TIME 1 1 -16152 65.842206 -245.32 <.0001 EVENT_TIME 2 1 -18193 67.851597 -268.13 <.0001 EVENT_TIME 3 1 -19274 70.616261 -272.94 <.0001 EVENT_TIME 4 1 -20521 73.783725 -278.12 <.0001 EVENT_TIME 5 1 -21161 77.62823 -272.6 <.0001 EVENT_TIME -1 0 0 . . . AGE 22 1 -33011 453.96922 -72.72 <.0001 AGE 23 1 -31030 417.04975 -74.4 <.0001 AGE 24 1 -28456 404.37074 -70.37 <.0001 AGE 25 1 -26188 398.02023 -65.8 <.0001 AGE 26 1 -24185 394.249 -61.34 <.0001 AGE 27 1 -22545 391.54082 -57.58 <.0001 AGE 28 1 -20992 389.47798 -53.9 <.0001 AGE 29 1 -19254 387.72049 -49.66 <.0001 AGE 30 1 -17465 386.09387 -45.23 <.0001 AGE 31 1 -15497 384.83827 -40.27 <.0001 AGE 32 1 -13497 383.4669 -35.2 <.0001 AGE 33 1 -11624 381.94541 -30.43 <.0001 AGE 34 1 -9663.065559 380.31737 -25.41 <.0001 AGE 35 1 -7642.137867 377.35071 -20.25 <.0001 AGE 36 1 -5740.093332 374.79132 -15.32 <.0001 AGE 37 1 -3900.210716 373.80018 -10.43 <.0001 AGE 38 1 -1949.436833 377.73752 -5.16 <.0001 AGE 39 0 0 . . . STD_YYYY 2002 1 36138 5578113 0.01 0.9948 STD_YYYY 2003 1 38566 5578113 0.01 0.9945 STD_YYYY 2004 1 40498 5578113 0.01 0.9942 STD_YYYY 2005 1 40989 5578113 0.01 0.9941 STD_YYYY 2006 1 42778 5578113 0.01 0.9939 STD_YYYY 2007 1 44914 5578113 0.01 0.9936 STD_YYYY 2008 1 46430 5578113 0.01 0.9934 STD_YYYY 2009 1 46597 5578113 0.01 0.9933 STD_YYYY 2010 1 46576 5578113 0.01 0.9933 STD_YYYY 2011 1 47046 5578113 0.01 0.9933 STD_YYYY 2012 1 47803 5578113 0.01 0.9932 STD_YYYY 2013 1 48222 5578113 0.01 0.9931 STD_YYYY 2014 1 49081 5578113 0.01 0.993 STD_YYYY 2015 1 49591 5578113 0.01 0.9929 STD_YYYY 2016 1 49746 5578113 0.01 0.9929 STD_YYYY 2017 1 50241 5578113 0.01 0.9928 STD_YYYY 2018 1 50654 5578113 0.01 0.9928 STD_YYYY 2019 1 52387 5578113 0.01 0.9925 As you can see the second table, dummies for year (STD_YYYY) are not omitted, and the intercept and STD_YYYY have high and same standard error. I think the problem has something to do with the weight. To specify C1_WEIGHT, I construct it to match the structure of age at event_time t = 0 with other samples (SAMPLE_CHT80). Therefore, samples within the same 'age at event_time t=0' have same value of weight, and I think it is related to event_time, age and year dummy.

jhmoon · ‎12-05-2023

Thank you for your help. I followed your recommendation 2 and confirmed that there are no negative weight, or other missing that affects the regression result. The results table for each regression codes are like below: PROC GLM DATA=SAMPLE_CHT70 ; CLASS EVENT_TIME(REF='-1') AGE(REF='39') STD_YYYY(REF='2019'); MODEL EARNINGS = EVENT_TIME AGE STD_YYYY/ SOLUTION CLPARM; WEIGHT C1_WEIGHT; OUTPUT OUT= PRED_CALC_CPI_WC1 PREDICTED=P RESIDUAL=R; ODS OUTPUT ParameterEstimates=PARAM_CALC_CPI_WC1; RUN; The GLM Procedure Class Level Information Class Levels Values EVENT_TIME 9 -3 -2 0 1 2 3 4 5 -1 AGE 18 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 STD_YYYY 18 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Number of Observations Read 5882292 Number of Observations Used 5713157 Weight: C1_WEIGHT Source DF Sum of Squares Mean Square F Value Pr > F Model 42 2.40E+14 5.71E+12 5122.4 <.0001 Error 5.71E+06 6.37E+15 1115212709 　　 Corrected Total 5.71E+06 6.61E+15 　　　 R-Square Coeff Var Root MSE EARNINGS Mean 0.036291 147.9133 33394.8 22577.28 Source DF Type I SS Mean Square F Value Pr > F EVENT_TIME 8 5.76E+13 7.20E+12 6459.7 <.0001 AGE 17 1.73E+14 1.02E+13 9143.87 <.0001 STD_YYYY 17 8.94E+12 5.25956E+11 471.62 <.0001 Source DF Type III SS Mean Square F Value Pr > F EVENT_TIME 8 1.90E+14 2.38E+13 21351.3 <.0001 AGE 17 3.09E+13 1.82E+12 1632.25 <.0001 STD_YYYY 17 8.94E+12 5.25956E+11 471.62 <.0001 Parameter Estimate 　 Standard t Value Pr > |t| 95% Confidence Limits Expected Value Error Intercept 57493.58092 B 598.5360918 96.06 <.0001 56320.47149 58666.69036 Intercept + [EVENT_TIME -1] + [AGE 39] + [STD_YYYY 2019] EVENT_TIME -3 1151.43048 B 67.746409 17 <.0001 1018.64993 1284.21103 [EVENT_TIME -3] - [EVENT_TIME -1] EVENT_TIME -2 1290.48506 B 63.7409616 20.25 <.0001 1165.55505 1415.41508 [EVENT_TIME -2] - [EVENT_TIME -1] EVENT_TIME 0 -6709.90206 B 63.2582534 -106.07 <.0001 -6833.88599 -6585.91814 [EVENT_TIME 0] - [EVENT_TIME -1] EVENT_TIME 1 -16152.14249 B 65.8422001 -245.32 <.0001 -16281.19086 -16023.09412 [EVENT_TIME 1] - [EVENT_TIME -1] EVENT_TIME 2 -18193.03079 B 67.8515907 -268.13 <.0001 -18326.01749 -18060.04408 [EVENT_TIME 2] - [EVENT_TIME -1] EVENT_TIME 3 -19273.77713 B 70.6162547 -272.94 <.0001 -19412.18248 -19135.37179 [EVENT_TIME 3] - [EVENT_TIME -1] EVENT_TIME 4 -20520.90805 B 73.7837183 -278.12 <.0001 -20665.52151 -20376.29459 [EVENT_TIME 4] - [EVENT_TIME -1] EVENT_TIME 5 -21161.09299 B 77.6282235 -272.6 <.0001 -21313.24154 -21008.94444 [EVENT_TIME 5] - [EVENT_TIME -1] EVENT_TIME -1 0 B . . . . . 　 AGE 22 -33011.33226 B 453.9691843 -72.72 <.0001 -33901.0957 -32121.56882 [AGE 22] - [AGE 39] AGE 23 -31030.13856 B 417.0497167 -74.4 <.0001 -31847.54116 -30212.73596 [AGE 23] - [AGE 39] AGE 24 -28456.20384 B 404.3707065 -70.37 <.0001 -29248.75603 -27663.65165 [AGE 24] - [AGE 39] AGE 25 -26188.46721 B 398.0201918 -65.8 <.0001 -26968.57262 -25408.36181 [AGE 25] - [AGE 39] AGE 26 -24184.50521 B 394.2489704 -61.34 <.0001 -24957.21915 -23411.79126 [AGE 26] - [AGE 39] AGE 27 -22544.71583 B 391.5407827 -57.58 <.0001 -23312.12182 -21777.30983 [AGE 27] - [AGE 39] AGE 28 -20991.74814 B 389.4779457 -53.9 <.0001 -21755.11105 -20228.38523 [AGE 28] - [AGE 39] AGE 29 -19253.74308 B 387.720454 -49.66 <.0001 -20013.66136 -18493.82479 [AGE 29] - [AGE 39] AGE 30 -17464.85106 B 386.093841 -45.23 <.0001 -18221.58124 -16708.12087 [AGE 30] - [AGE 39] AGE 31 -15496.64241 B 384.8382356 -40.27 <.0001 -16250.91165 -14742.37317 [AGE 31] - [AGE 39] AGE 32 -13496.68391 B 383.4668709 -35.2 <.0001 -14248.26533 -12745.1025 [AGE 32] - [AGE 39] AGE 33 -11624.09823 B 381.945376 -30.43 <.0001 -12372.69757 -10875.49889 [AGE 33] - [AGE 39] AGE 34 -9663.06563 B 380.3173421 -25.41 <.0001 -10408.47408 -8917.65718 [AGE 34] - [AGE 39] AGE 35 -7642.13794 B 377.3506788 -20.25 <.0001 -8381.73183 -6902.54404 [AGE 35] - [AGE 39] AGE 36 -5740.0934 B 374.7912853 -15.32 <.0001 -6474.67097 -5005.51582 [AGE 36] - [AGE 39] AGE 37 -3900.21078 B 373.8001485 -10.43 <.0001 -4632.84576 -3167.57579 [AGE 37] - [AGE 39] AGE 38 -1949.43689 B 377.7374853 -5.16 <.0001 -2689.78891 -1209.08486 [AGE 38] - [AGE 39] AGE 39 0 B . . . . . 　 STD_YYYY 2002 -16249.4725 B 723.7739728 -22.45 <.0001 -17668.04372 -14830.90128 [STD_YYYY 2002] - [STD_YYYY 2019] STD_YYYY 2003 -13821.10917 B 715.5893301 -19.31 <.0001 -15223.63878 -12418.57956 [STD_YYYY 2003] - [STD_YYYY 2019] STD_YYYY 2004 -11888.85238 B 712.2320858 -16.69 <.0001 -13284.80191 -10492.90285 [STD_YYYY 2004] - [STD_YYYY 2019] STD_YYYY 2005 -11398.11108 B 710.2950105 -16.05 <.0001 -12790.26401 -10005.95815 [STD_YYYY 2005] - [STD_YYYY 2019] STD_YYYY 2006 -9609.6762 B 708.9337937 -13.56 <.0001 -10999.1612 -8220.19121 [STD_YYYY 2006] - [STD_YYYY 2019] STD_YYYY 2007 -7473.76677 B 707.8289783 -10.56 <.0001 -8861.08636 -6086.44717 [STD_YYYY 2007] - [STD_YYYY 2019] STD_YYYY 2008 -5956.85432 B 706.8830583 -8.43 <.0001 -7342.31995 -4571.38869 [STD_YYYY 2008] - [STD_YYYY 2019] STD_YYYY 2009 -5790.10949 B 706.004101 -8.2 <.0001 -7173.85239 -4406.36658 [STD_YYYY 2009] - [STD_YYYY 2019] STD_YYYY 2010 -5811.82232 B 705.1423246 -8.24 <.0001 -7193.87617 -4429.76847 [STD_YYYY 2010] - [STD_YYYY 2019] STD_YYYY 2011 -5341.59152 B 704.5219164 -7.58 <.0001 -6722.42939 -3960.75364 [STD_YYYY 2011] - [STD_YYYY 2019] STD_YYYY 2012 -4584.69534 B 703.8424324 -6.51 <.0001 -5964.20145 -3205.18923 [STD_YYYY 2012] - [STD_YYYY 2019] STD_YYYY 2013 -4165.11988 B 703.2508086 -5.92 <.0001 -5543.46643 -2786.77333 [STD_YYYY 2013] - [STD_YYYY 2019] STD_YYYY 2014 -3306.63579 B 702.6738482 -4.71 <.0001 -4683.85151 -1929.42006 [STD_YYYY 2014] - [STD_YYYY 2019] STD_YYYY 2015 -2796.6873 B 700.2987696 -3.99 <.0001 -4169.24796 -1424.12665 [STD_YYYY 2015] - [STD_YYYY 2019] STD_YYYY 2016 -2641.52688 B 700.5926249 -3.77 0.0002 -4014.66349 -1268.39028 [STD_YYYY 2016] - [STD_YYYY 2019] STD_YYYY 2017 -2145.88732 B 704.80958 -3.04 0.0023 -3527.28901 -764.48564 [STD_YYYY 2017] - [STD_YYYY 2019] STD_YYYY 2018 -1733.12014 B 722.7610174 -2.4 0.0165 -3149.70601 -316.53428 [STD_YYYY 2018] - [STD_YYYY 2019] STD_YYYY 2019 0 B . . . . . 　 Note: The X'X matrix has been found to be singular, and a generalized inverse was used to solve the normal equations. Terms whose estimates are followed by the letter 'B' are not uniquely estimable. PROC GLMSELECT DATA=SAMPLE_CHT70 ; CLASS EVENT_TIME(REF='-1') AGE STD_YYYY ; MODEL CALC_CPI = EVENT_TIME AGE STD_YYYY/ SELECTION=NONE ; WEIGHT C1_WEIGHT; OUTPUT OUT= PRED_CALC_CPI_WC1 PREDICTED=P RESIDUAL=R; ODS OUTPUT ParameterEstimates=PARAM_CALC_CPI_WC1; RUN; The GLMSELECT Procedure Data Set SAMPLE_CHT70 Weight C1_WEIGHT Dependent Variable EARNINGS Selection Method None Number of Observations Read 5882292 Number of Observations Used 5713157 Class Level Information Class Levels Values EVENT_TIME 9 -3 -2 0 1 2 3 4 5 -1 AGE 18 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 STD_YYYY 18 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Dimensions Number of Effects 4 Number of Parameters 46 The GLMSELECT Procedure Least Squares Summary Step Effect Number Number SBC Entered Effects In Parms In 0 Intercept 1 1 119229427 1 EVENT_TIME 2 9 119179530 2 AGE 3 26 119026637 3 STD_YYYY 4 44 119018905* * Optimal Value of Criterion The GLMSELECT Procedure Least Squares Model (No Selection) Analysis of Variance Source DF Sum of Mean F Value Pr > F Squares Square Model 43 2.40E+14 5.58E+12 5003.28 <.0001 Error 5.71E+06 6.37E+15 1.115E+09 　　 Corrected Total 5.71E+06 6.61E+15 　　　 Root MSE 33395 Dependent Mean 22577 R-Square 0.0363 Adj R-Sq 0.0363 AIC 124731467 AICC 124731467 SBC 119018905 Parameter Estimates Parameter DF Estimate Standard t Value Pr > |t| Error Intercept 1 5106.229845 5578113 0 0.9993 EVENT_TIME -3 1 1151.430482 67.746415 17 <.0001 EVENT_TIME -2 1 1290.485072 63.740967 20.25 <.0001 EVENT_TIME 0 1 -6709.902053 63.258259 -106.07 <.0001 EVENT_TIME 1 1 -16152 65.842206 -245.32 <.0001 EVENT_TIME 2 1 -18193 67.851597 -268.13 <.0001 EVENT_TIME 3 1 -19274 70.616261 -272.94 <.0001 EVENT_TIME 4 1 -20521 73.783725 -278.12 <.0001 EVENT_TIME 5 1 -21161 77.62823 -272.6 <.0001 EVENT_TIME -1 0 0 . . . AGE 22 1 -33011 453.96922 -72.72 <.0001 AGE 23 1 -31030 417.04975 -74.4 <.0001 AGE 24 1 -28456 404.37074 -70.37 <.0001 AGE 25 1 -26188 398.02023 -65.8 <.0001 AGE 26 1 -24185 394.249 -61.34 <.0001 AGE 27 1 -22545 391.54082 -57.58 <.0001 AGE 28 1 -20992 389.47798 -53.9 <.0001 AGE 29 1 -19254 387.72049 -49.66 <.0001 AGE 30 1 -17465 386.09387 -45.23 <.0001 AGE 31 1 -15497 384.83827 -40.27 <.0001 AGE 32 1 -13497 383.4669 -35.2 <.0001 AGE 33 1 -11624 381.94541 -30.43 <.0001 AGE 34 1 -9663.065559 380.31737 -25.41 <.0001 AGE 35 1 -7642.137867 377.35071 -20.25 <.0001 AGE 36 1 -5740.093332 374.79132 -15.32 <.0001 AGE 37 1 -3900.210716 373.80018 -10.43 <.0001 AGE 38 1 -1949.436833 377.73752 -5.16 <.0001 AGE 39 0 0 . . . STD_YYYY 2002 1 36138 5578113 0.01 0.9948 STD_YYYY 2003 1 38566 5578113 0.01 0.9945 STD_YYYY 2004 1 40498 5578113 0.01 0.9942 STD_YYYY 2005 1 40989 5578113 0.01 0.9941 STD_YYYY 2006 1 42778 5578113 0.01 0.9939 STD_YYYY 2007 1 44914 5578113 0.01 0.9936 STD_YYYY 2008 1 46430 5578113 0.01 0.9934 STD_YYYY 2009 1 46597 5578113 0.01 0.9933 STD_YYYY 2010 1 46576 5578113 0.01 0.9933 STD_YYYY 2011 1 47046 5578113 0.01 0.9933 STD_YYYY 2012 1 47803 5578113 0.01 0.9932 STD_YYYY 2013 1 48222 5578113 0.01 0.9931 STD_YYYY 2014 1 49081 5578113 0.01 0.993 STD_YYYY 2015 1 49591 5578113 0.01 0.9929 STD_YYYY 2016 1 49746 5578113 0.01 0.9929 STD_YYYY 2017 1 50241 5578113 0.01 0.9928 STD_YYYY 2018 1 50654 5578113 0.01 0.9928 STD_YYYY 2019 1 52387 5578113 0.01 0.9925 As you can see the second result (glmselect), variables for year dummies and intercept has same and high standard error. And to specify C1_WEIGHT, I construct it to match the structure of age at event time t=0, so that I can compare the result to that of other samples (SAMPLE_CHT80). I think this problem has something to do with the weight, because each observations' weight value is same if they had same ages at event time t=0.

jhmoon · ‎12-03-2023

Dear all, My problem is that when I did a event study analysis with code 'glm' and 'glmselect', the results are not the same, and I want to know why. Specifically, I use event-study method, and the outcome variable is earnings, RHS variables are 9 event time dummies, 18 year dummies and 18 age dummies. Everything is fine, but the results become weird when the weights are applied. (in a way that reference dummies are not omitted.) Here is the code I used. /*Overall results */ /*glmselect has a problem in this case*/ PROC GLM DATA=SAMPLE_CHT70 ; CLASS EVENT_TIME(REF='-1') AGE(REF='39') STD_YYYY(REF='2019'); MODEL EARNINGS = EVENT_TIME AGE STD_YYYY/ SOLUTION CLPARM; WEIGHT C1_WEIGHT; RUN; PROC GLMSELECT DATA=SAMPLE_CHT70 ; CLASS EVENT_TIME(REF='-1') AGE(REF='39') STD_YYYY(REF='2019') ; MODEL EARNINGS = EVENT_TIME AGE STD_YYYY/ SELECTION=NONE ; WEIGHT C1_WEIGHT; RUN; The results are like below: 1) With glm, reference dummy, "Event_time -1", "Age 39", "Std_yyyy 2019", are omitted, so that other estimates are well-estimated I think. 2) With glmselect, "Event_time -1", "Age 39" are omitted, but "Std_yyyy 2019" are not. And without applying weight, there are all right. Does anyone know about this type of issue?

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Re: problem in glm and glmselect (revised)

Re: problem in glm and glmselect (revised)

Re: problem in glm and glmselect (revised)

Re: problem in glm and glmselect (revised)

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problem in glm and glmselect (revised)

Re: Different results between 'glm' and 'glmselect'

Re: Different results between 'glm' and 'glmselect'

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Re: problem in glm and glmselect (revised)

Re: Different results between 'glm' and 'glmselect'

Re: Different results between 'glm' and 'glmselect'

Re: Different results between 'glm' and 'glmselect'

Re: Different results between 'glm' and 'glmselect'

Re: problem in glm and glmselect (revised)

Re: problem in glm and glmselect (revised)

Re: problem in glm and glmselect (revised)

Re: problem in glm and glmselect (revised)

Re: problem in glm and glmselect (revised)

Re: Different results between 'glm' and 'glmselect'

problem in glm and glmselect (revised)

Re: Different results between 'glm' and 'glmselect'

Re: Different results between 'glm' and 'glmselect'

Re: Different results between 'glm' and 'glmselect'

Different results between 'glm' and 'glmselect'