Never mind. I will think something up. I think that you don't understand what is my problem that I have to code it. Anyway, thank you for help for previous problem. ... View more

 But that is not correct result!   My result should be the result in which is all three conditions fulfilled: ee < 0.01 err_stage2 is minimal dist_prov is minimal   Therefore I need to put all three conditions.   In the first step: distance and err_prev should be set to 1E6; in next step if condition ee < 0.01 and err_stage2<err_prev, then err_stage should be set to te err_stage2 from the first step. In case err_stage2 = err_prev and dist_prov < distance, then err_stage should be set to te err_stage2  from the fist step and distance should be set to dist_prov from the first step.   This shoul be running until non of conditions are met, and the code should return previous observation. ... View more

data STEP; infile datalines delimiter=','; input step $ dist_prov $ err_stage2 $ ee; datalines; 0.0018,0.713688038,-21,0.0008025069 0.0108,0.5895856469,-97,0.0037068175 0.0109,0.589033195,-97,0.0037068175 0.011,0.588498118,-97,0.0037068175 0.0111,0.5879803893,-100,0.0038214613 0.0112,0.5874800437,-101,0.0038596759 0.0113,0.5869970738,-101,0.0038596759 0.0114,0.5865313784,-102,0.0038978906 0.0115,0.5860828259,-102,0.0038978906 0.0116,0.5856514384,-103,0.0039361052 0.0117,0.5852372379,-103,0.0039361052 0.0019,0.7114179585,-22,0.0008407215 0.0118,0.5848402043,-104,0.0039743198 0.0119,0.5844602398,-104,0.0039743198 0.012,0.5840973151,-104,0.0039743198 0.0121,0.5837514453,-104,0.0039743198 0.0122,0.5834226505,-104,0.0039743198 0.0123,0.5831109592,-104,0.0039743198 0.0124,0.5828164182,-104,0.0039743198 0.0125,0.5825389541,-104,0.0039743198 0.0126,0.5822784975,-105,0.0040125344 0.0127,0.5820350474,-105,0.0040125344 0.002,0.7091705406,-22,0.0008407215 0.0128,0.5818086221,-105,0.0040125344 0.0129,0.5815993391,-106,0.004050749 0.013,0.5814072808,-106,0.004050749 0.0131,0.5812324477,-109,0.0041653928 0.0132,0.5810747073,-111,0.0042418221 0.0133,0.5809340598,-111,0.0042418221 0.0134,0.5808108228,-111,0.0042418221 0.0135,0.5807051824,-111,0.0042418221 0.0136,0.580617169,-112,0.0042800367 0.0137,0.5805464088,-112,0.0042800367 0.0021,0.7069457815,-23,0.0008789361 0.0138,0.580492745,-112,0.0042800367 0.0139,0.5804560056,-112,0.0042800367 0.014,0.5804362434,-112,0.0042800367 0.0141,0.5804334986,-113,0.0043182513 0.0142,0.5804477747,-113,0.0043182513 0.0143,0.580479013,-113,0.0043182513 0.0144,0.5805272225,-113,0.0043182513 0.0145,0.5805924352,-113,0.0043182513 0.0146,0.580674683,-116,0.0044328951 0.0147,0.5807740923,-116,0.0044328951 0.0022,0.7047432879,-25,0.0009553653 0.0148,0.5808907457,-116,0.0044328951 0.0149,0.5810246481,-116,0.0044328951 0.015,0.5811757607,-116,0.0044328951 0.0151,0.5813440406,-116,0.0044328951 0.0152,0.5815294066,-116,0.0044328951 0.0153,0.5817318128,-116,0.0044328951 0.0154,0.5819512482,-116,0.0044328951 0.0155,0.5821876916,-116,0.0044328951 0.0156,0.5824410173,-116,0.0044328951 0.0157,0.5827112049,-116,0.0044328951 0.0023,0.7025629119,-26,0.0009935799 0.0158,0.5829982404,-116,0.0044328951 0.0159,0.5833022193,-116,0.0044328951 0.016,0.583623084,-116,0.0044328951 0.0161,0.5839604872,-116,0.0044328951 0.0162,0.5843140187,-116,0.0044328951 0.0163,0.5846835963,-116,0.0044328951 0.0164,0.5850691749,-116,0.0044328951 0.0165,0.5854707041,-117,0.0044711098 0.0166,0.5858877426,-118,0.0045093244 0.0167,0.5863201851,-118,0.0045093244 0.0024,0.7004047561,-31,0.001184653 0.0168,0.5867679921,-118,0.0045093244 0.0169,0.587231135,-118,0.0045093244 0.017,0.5877096111,-118,0.0045093244 0.0171,0.5882034291,-118,0.0045093244 0.0172,0.5887125685,-118,0.0045093244 0.0173,0.589236972,-118,0.0045093244 0.0174,0.5897765904,-118,0.0045093244 0.0174,0.5897765904,-118,0.0045093244 0.0175,0.5903313671,-118,0.0045093244 0.0176,0.5909011571,-120,0.0045857536 0.0025,0.6982688974,-31,0.001184653 0.0177,0.5914858743,-120,0.0045857536 0.0178,0.5920855059,-120,0.0045857536 0.0179,0.5927000213,-120,0.0045857536 0.018,0.5933293857,-120,0.0045857536 0.0181,0.59397357,-120,0.0045857536 0.0182,0.5946325383,-120,0.0045857536 0.0183,0.5953062513,-121,0.0046239682 0.0184,0.59599464,-121,0.0046239682 0.0185,0.5966976499,-121,0.0046239682 0.0186,0.5974153152,-121,0.0046239682 0.0026,0.6961553577,-33,0.0012610822 0.0187,0.5981474667,-121,0.0046239682 0.0188,0.5988937947,-121,0.0046239682 0.0189,0.5996542733,-121,0.0046239682 0.019,0.6004289303,-122,0.0046621828 0.0191,0.6012179197,-122,0.0046621828 0.0192,0.602021214,-122,0.0046621828 0.0193,0.602838726,-122,0.0046621828 0.0194,0.6036703648,-122,0.0046621828 0.0195,0.6045161413,-122,0.0046621828 0.0196,0.6053762111,-122,0.0046621828 0.0027,0.6940639582,-36,0.0013757261 0.0197,0.6062504979,-122,0.0046621828 0.0198,0.6071389425,-124,0.004738612 0.0199,0.6080414891,-124,0.004738612 0.02,0.6089580546,-124,0.004738612 0.0201,0.6098885958,-124,0.004738612 0.0202,0.6108330459,-124,0.004738612 0.0203,0.6117913342,-124,0.004738612 0.0204,0.6127636336,-124,0.004738612 0.0205,0.6137501761,-124,0.004738612 0.0206,0.6147505274,-124,0.004738612 0.001,0.7326723142,-12,0.0004585754 0.0028,0.6919946245,-36,0.0013757261 0.0207,0.6157645942,-124,0.004738612 0.0208,0.6167922683,-127,0.0048532559 0.0209,0.6178335216,-127,0.0048532559 0.021,0.6188883503,-127,0.0048532559 0.0211,0.6199567182,-127,0.0048532559 0.0212,0.6210385425,-127,0.0048532559 0.0213,0.6221337216,-127,0.0048532559 0.0214,0.6232421898,-127,0.0048532559 0.0215,0.6243638879,-127,0.0048532559 0.0216,0.6254986921,-127,0.0048532559 0.0029,0.6899472953,-37,0.0014139407 0.0217,0.6266465883,-127,0.0048532559 0.0218,0.6278075507,-127,0.0048532559 0.0219,0.6289814866,-127,0.0048532559 0.022,0.6301683119,-127,0.0048532559 0.0221,0.6313678824,-127,0.0048532559 0.0222,0.6325798847,-127,0.0048532559 0.0223,0.633804373,-127,0.0048532559 0.0224,0.6350417467,-127,0.0048532559 0.0225,0.6362919826,-128,0.0048914705 0.0226,0.6375550963,-128,0.0048914705 0.003,0.6879216749,-37,0.0014139407 0.0227,0.6388310724,-128,0.0048914705 0.0228,0.6401197903,-128,0.0048914705 0.0229,0.6414211309,-128,0.0048914705 0.023,0.6427350077,-129,0.0049296851 0.0231,0.6440613258,-133,0.0050825436 0.0232,0.6454000338,-139,0.0053118312 0.0233,0.6467511525,-139,0.0053118312 0.0234,0.648114648,-139,0.0053118312 0.0235,0.6494904548,-139,0.0053118312 0.0236,0.6508783126,-139,0.0053118312 0.0031,0.6859176687,-41,0.0015667991 0.0237,0.6522780045,-139,0.0053118312 0.0238,0.6536894544,-139,0.0053118312 0.0239,0.655112577,-139,0.0053118312 0.024,0.6565472328,-139,0.0053118312 0.0241,0.6579933356,-139,0.0053118312 0.0242,0.6594507924,-139,0.0053118312 0.0243,0.660919541,-139,0.0053118312 0.0244,0.6623994857,-139,0.0053118312 0.0245,0.663890631,-139,0.0053118312 0.0246,0.6653929524,-140,0.0053500459 0.0032,0.6839353239,-42,0.0016050138 0.0247,0.6669063126,-140,0.0053500459 0.0248,0.6684306613,-140,0.0053500459 0.0249,0.6699659195,-140,0.0053500459 0.0033,0.6819747122,-43,0.0016432284 0.0034,0.6800358955,-44,0.001681443 0.0035,0.678118918,-45,0.0017196576 0.0036,0.676223708,-45,0.0017196576 0.0037,0.6743500794,-45,0.0017196576 0.0011,0.7302183254,-13,0.00049679 0.0038,0.6724978203,-46,0.0017578722 0.0039,0.6706668561,-52,0.0019871599 0.004,0.6688570361,-54,0.0020635891 0.0041,0.6670675998,-54,0.0020635891 0.0042,0.6652980377,-54,0.0020635891 0.0043,0.6635481238,-54,0.0020635891 0.0044,0.6618178068,-54,0.0020635891 0.0045,0.6601071536,-56,0.0021400183 0.0046,0.6584158953,-57,0.002178233 0.0047,0.6567436522,-58,0.0022164476 0.0012,0.7277881194,-14,0.0005350046 0.0048,0.6550905318,-58,0.0022164476 0.0049,0.6534566204,-58,0.0022164476 0.005,0.6518420547,-59,0.0022546622 0.0051,0.6502468857,-59,0.0022546622 0.0052,0.6486711395,-60,0.0022928768 0.0053,0.6471148905,-61,0.0023310914 0.0054,0.6455781515,-61,0.0023310914 0.0055,0.6440607061,-61,0.0023310914 0.0056,0.6425625301,-62,0.002369306 0.0057,0.6410836022,-62,0.002369306 0.0013,0.7253810198,-15,0.0005732192 0.0058,0.6396240639,-62,0.002369306 0.0059,0.6381839194,-65,0.0024839499 0.006,0.6367627444,-66,0.0025221645 0.0061,0.6353604102,-67,0.0025603791 0.0062,0.6339770941,-76,0.0029043106 0.0063,0.6326126929,-82,0.0031335983 0.0064,0.6312667955,-82,0.0031335983 0.0065,0.6299390589,-82,0.0031335983 0.0066,0.6286292918,-82,0.0031335983 0.0067,0.6273375439,-83,0.0031718129 0.0014,0.722996671,-16,0.0006114338 0.0068,0.6260637108,-83,0.0031718129 0.0069,0.6248075526,-83,0.0031718129 0.007,0.6235690708,-83,0.0031718129 0.0071,0.622348405,-83,0.0031718129 0.0072,0.6211456171,-83,0.0031718129 0.0073,0.6199607504,-86,0.0032864567 0.0074,0.6187935948,-86,0.0032864567 0.0075,0.6176438147,-86,0.0032864567 0.0076,0.6165113016,-87,0.0033246714 0.0077,0.6153958048,-87,0.0033246714 0.0015,0.7206351271,-16,0.0006114338 0.0078,0.6142973488,-87,0.0033246714 0.0079,0.6132161784,-87,0.0033246714 0.008,0.6121525688,-87,0.0033246714 0.0081,0.6111068155,-87,0.0033246714 0.0082,0.6100790422,-87,0.0033246714 0.0083,0.6090693428,-87,0.0033246714 0.0084,0.6080776786,-89,0.0034011006 0.0085,0.6071040243,-89,0.0034011006 0.0086,0.6061483192,-90,0.0034393152 0.0087,0.6052104875,-90,0.0034393152 0.0016,0.7182964731,-21,0.0008025069 0.0088,0.6042903653,-90,0.0034393152 0.0089,0.6033880189,-92,0.0035157444 0.009,0.6025034625,-95,0.0036303883 0.0091,0.6016366857,-96,0.0036686029 0.0092,0.6007876556,-96,0.0036686029 0.0093,0.5999562669,-96,0.0036686029 0.0094,0.5991425134,-96,0.0036686029 0.0095,0.5983464205,-96,0.0036686029 0.0096,0.5975678942,-97,0.0037068175 0.0097,0.5968068381,-97,0.0037068175 0.0017,0.7159807906,-21,0.0008025069 0.0098,0.5960632359,-97,0.0037068175 0.0099,0.5953371315,-97,0.0037068175 0.01,0.594628569,-97,0.0037068175 0.0101,0.593937541,-97,0.0037068175 0.0102,0.5932638825,-97,0.0037068175 0.0103,0.5926075631,-97,0.0037068175 0.0104,0.5919685772,-97,0.0037068175 0.0105,0.591346914,-97,0.0037068175 0.0106,0.5907425438,-97,0.0037068175 0.0107,0.5901554495,-97,0.0037068175 ; RUN; But, what if I have this data. It won't work.   Therefore, I think I have to add this kind of code/condition: if ee < 0.01 then do; IF err_stage2 < err_prev then do; beta = step; err_prev = err_stage2; distance = dist_prov; new_quantile = 1-(SUM_StgChange_CT)/26168; change_quant = (SUM_StgChange_CT-SUM_StgChange)/26168; end; else if (err_stage2=err_prev) & (dist_prov<distance) then do; beta = step; distance = dist_prov; new_quantile = 1-(SUM_StgChange_CT)/26168; change_quant = (SUM_StgChange_CT-SUM_StgChange)/26168; end;   However, sill don't know how to create code that result in one observation.   ... View more

data step; infile datalines delimiter=','; input step $ dist_prov $ err_stage2 $ ee; datalines; 0,0.758514812,10,0.000382146 0.0018,0.713688038,-21,0.0008025069 0.0108,0.5895856469,-97,0.0037068175 0.0109,0.589033195,-97,0.0037068175 0.011,0.588498118,-97,0.0037068175 0.0111,0.5879803893,-100,0.0038214613 0.0112,0.5874800437,-101,0.0038596759 0.0113,0.5869970738,-101,0.0038596759 0.0114,0.5865313784,-102,0.0038978906 0.0115,0.5860828259,-102,0.0038978906 0.0116,0.5856514384,-103,0.0039361052 0.0117,0.5852372379,-103,0.0039361052 0.0019,0.7114179585,-22,0.0008407215 ; run; This is my data step.   Expected result: step = 0.0117   Is it ok like this?   I am filing like learning it. ... View more

Well, my final step that I will choose for solution of my problem is the step which meet next conditions: ee < 0.01; err_stage2 is minimal for that step; prov _prov is minimal for that step; In the first line, I will set err_prev and distance to some big number, and in every next line I will set new values for err_prev and distance which are equal to err_stage2 and dist_prov of the previous step/row. That was my idea. ... View more

Thank You! This means a lot to me since I still don't have so much experience and knowledge with macros in SAS and programing in SAS in general! It works now! Finally, I can see the table like this: step sum_StgChange SUM_StgChange_CT SSQ2 dist_prov err_stage2 ee 0 2608 2618 0.57534472 0.758514812 10 0.000382146 0.0018 2608 2587 0.509350616 0.713688038 -21 0.000802507 0.0108 2608 2511 0.347611235 0.589585647 -97 0.003706818 0.0109 2608 2511 0.346960105 0.589033195 -97 0.003706818 And now I want some optimization code with some conditions to be fulfilled and as result of the code I just want to see one line with most optimal step. I tried to do it with this code: %LET distance = 1E6; %LET err_prev = 1E6; %LET new_quantile = 0; %LET change_quant = 0; %LET beta = 0; DATA BETA_FINAL; SET bete; err_prev = 1E6; IF ee < 0.01 THEN DO; IF(err_stage2 < err_prev) THEN DO; beta = step; err_prev = err_stage2; distance = dist_prov; new_quantile = 1-(SUM_StgChange_CT)/26168; change_quant = (SUM_StgChange_CT-SUM_StgChange)/26168; end; else if (err_stage2=err_prev) & (dist_prov<distance) then do; beta = step; distance = dist_prov; new_quantile = 1-(SUM_StgChange_CT)/26168; change_quant = (SUM_StgChange_CT-SUM_StgChange)/26168; end; END; RUN; but it won't work. It will calculate all from the code but for every step, for every row in the table. And I don't want that, I want just one line/row where all conditions are fulfilled. Do you have any suggestions how to do this? Thank you! ... View more

Great!   Last but not the least, when this is finished I don't have any output.   Particularly, I am creating tables in makro- 242 tables, that finally, I want to join, and I would do like you already proposed by running:   data bete; set ctage_obs:; run;   But it won't work, unfortinately.   What is the problem?   This is the code I run:   %macro makro(korak,broj); DATA CTAge; SET obs_stg_change; step=((1-alpha)/log(50))/(50-1); y= alpha+ &korak*log(Age); IF(I1=1) THEN min_Threshold = min(y,PDThreshold); IF(I2=1) THEN max_Threshold = max(y,PDThreshold); min_max_Threshold=COALESCE(min_Threshold, max_Threshold); IF PDPuncCurr > min_max_Threshold THEN StgChange_CT = 1; ELSE StgChange_CT = 0; RUN; PROC SQL; CREATE TABLE CTAge_obs&broj AS SELECT &korak AS step, min(t1.sum_StgChange) as sum_StgChange, (SUM(t1.StgChange_CT)) AS SUM_StgChange_CT, /*a1*//* count # of obs which change stage */ /* (SUM(t1.StgChange_CT) - t1.sum_StgChange) AS PROVERA,*/ /* (SUM(abs(t1.StgChange_CT-t1.StgChange))) AS SUM_StgChange_DIFF,*/ /*Calculate distance between old CT and new one */ (SUM((t1.min_max_Threshold-t1.PDThreshold)**2)) AS SSQ2, /*sum of square*/ SQRT(SUM((t1.min_max_Threshold-t1.PDThreshold)**2)) AS dist_prov, /* square root of the distance */ ABS(SUM(t1.StgChange_CT) - min(t1.sum_StgChange)) AS err_stage2, /* Sum of obs which change stage */ ABS(SUM(t1.StgChange_CT)-sum(t1.StgChange))/MIN(t1.no_obs) AS ee FROM WORK.CTAge t1; QUIT; %mend;   %macro makro(i,j); %put &=i,&=j; %mend; data _null_; call execute('%nrstr(%makro(0,1))'); i = .001; do j = 2 to 242; call execute(cats('%nrstr(%makro(',i,',',j,'))')); i = i + .0001; end; run; data bete; set ctage_obs:; run;   And I can see error in my log:   ERROR: The data set list (WORK.ctage_obs:) does not contain any members. ... View more

Again, this is not what I wanted, because when I run it I can see that for one I value there is j different tables and I don't want that.   I just want that every I value has it's index. I have 242 different I values so I need 242 different coefficients for created tables, for every I. Whenever I value is changes, also j values has to be changed to next integer. So:   i            j 0          1 0.001   2 0.0011 3 0.0012 4   and so on.   Any suggestion? ... View more

Actually, the idea behind this was to add j in order to have better naming for every table, so as to know which table is for which step and how to join them.    Do You have any suggestion how to change it?   The code that I wrote is:   %macro makro(korak,broj); DATA CTAge; SET obs_stg_change; step=((1-alpha)/log(50))/(50-1); y= alpha+ &korak*log(Age); IF(I1=1) THEN min_Threshold = min(y,PDThreshold); IF(I2=1) THEN max_Threshold = max(y,PDThreshold); min_max_Threshold=COALESCE(min_Threshold, max_Threshold); IF PDPuncCurr > min_max_Threshold THEN StgChange_CT = 1; ELSE StgChange_CT = 0; RUN; /*0.5757232999 SUM SQUARE*/ PROC SQL; CREATE TABLE CTAge_obs&broj AS SELECT &korak AS step, min(t1.sum_StgChange) as sum_StgChange, (SUM(t1.StgChange_CT)) AS SUM_StgChange_CT, /*a1*//* count # of obs which change stage */ /* (SUM(t1.StgChange_CT) - t1.sum_StgChange) AS PROVERA,*/ /* (SUM(abs(t1.StgChange_CT-t1.StgChange))) AS SUM_StgChange_DIFF,*/ /*Calculate distance between old CT and new one */ (SUM((t1.min_max_Threshold-t1.PDThreshold)**2)) AS SSQ2, /*sum of square*/ SQRT(SUM((t1.min_max_Threshold-t1.PDThreshold)**2)) AS dist_prov, /* square root of the distance */ ABS(SUM(t1.StgChange_CT) - min(t1.sum_StgChange)) AS err_stage2, /* Sum of obs which change stage */ ABS(SUM(t1.StgChange_CT)-sum(t1.StgChange))/MIN(t1.no_obs) AS ee FROM WORK.CTAge t1; QUIT; %mend;   %makro(0,1); %makro(0.001,2); %makro(0.0011,3); %makro(0.0012,4); %makro(0.0013,5); %makro(0.0014,6); %makro(0.0015,7); %makro(0.0016,8); %makro(0.0017,9); %makro(0.0018,10); %makro(0.0019,11); %makro(0.002,12); %makro(0.0021,13); %makro(0.0022,14); %makro(0.0023,15); %makro(0.0024,16); %makro(0.0025,17); %makro(0.0026,18); %makro(0.0027,19); ...   and so on until the 242. table and:   data bete; set ctage_obs:; run;   Do You have any suggestion what would be the best solution instead of writting %makro(x,y); for 242 times?   Thanks a lot! ... View more

Also, when I run the proposed solution, it looks like it is never ending loop. ... View more

1 The SAS System Tuesday, April 28, 2020 12:45:00 PM 1 ;*';*";*/;quit;run; 2 OPTIONS PAGENO=MIN; 3 %LET _CLIENTTASKLABEL='Program'; 4 %LET _CLIENTPROCESSFLOWNAME='Process Flow'; 5 %LET _CLIENTPROJECTPATH='\\srvnas\common\CRO\Strategic Risk\Credit risk Governance and Models\Credit Risk Modeling\IFRS 5 ! 9\03 IFRS 9 Modeling\02 IFRS 9 TL modeling\QE & TL 2020\Transfer logic\IFRS 9 2020 TL v8.egp'; 6 %LET _CLIENTPROJECTPATHHOST='UCB29756'; 7 %LET _CLIENTPROJECTNAME='IFRS 9 2020 TL v8.egp'; 8 %LET _SASPROGRAMFILE=''; 9 %LET _SASPROGRAMFILEHOST=''; 10 11 ODS _ALL_ CLOSE; 12 OPTIONS DEV=PNG; 13 GOPTIONS XPIXELS=0 YPIXELS=0; 14 FILENAME EGSR TEMP; 15 ODS tagsets.sasreport13(ID=EGSR) FILE=EGSR 16 STYLE=HtmlBlue 17 STYLESHEET=(URL="file:///C:/Program%20Files%20(x86)/SASHome/x86/SASEnterpriseGuide/7.1/Styles/HtmlBlue.css") 18 NOGTITLE 19 NOGFOOTNOTE 20 GPATH=&sasworklocation SYMBOLGEN: Macro variable SASWORKLOCATION resolves to "F:\WORK\_TD4820_SRVSAS06_\Prc2/" 21 ENCODING=UTF8 22 options(rolap="on") 23 ; NOTE: Writing TAGSETS.SASREPORT13(EGSR) Body file: EGSR 24 25 GOPTIONS ACCESSIBLE; 26 options mprint symbolgen mlogic; 27 options compress=binary; 28 29 30 31 DATA QntRegTest; 32 SET QntRegCorp /*QntRegCorpNonRev*/ /*QntRegCorpREV*/; 33 34 /*ISTA JE PODELA KOD SVA 3 segmenta*/ 35 IF RatingClassFirstAv in (1,2,3,4,5) THEN I1 = 1; 36 ELSE I1 = 0; 37 IF RatingClassFirstAv in (6,7,8) THEN I2 = 1; 38 ELSE I2 = 0; 39 40 LnAge1 = LnAge*I1; 41 LnAge2 = LnAge*I2; 42 43 RUN; NOTE: There were 26168 observations read from the data set WORK.QNTREGCORP. NOTE: The data set WORK.QNTREGTEST has 26168 observations and 79 variables. NOTE: Compressing data set WORK.QNTREGTEST decreased size by 83.41 percent. Compressed is 106 pages; un-compressed would require 639 pages. NOTE: DATA statement used (Total process time): real time 0.25 seconds cpu time 0.25 seconds 44 45 PROC QUANTREG DATA=QntRegTest OUTest=QntRegEst algorithm=interior (tolerance=1e-5) ci=resampling; 2 The SAS System Tuesday, April 28, 2020 12:45:00 PM 46 MODELLnAge12WORM: MODEL AP1 = LnPD0 LnAge1 LnAge2 / quantile=0.9 /*plots=all*/; 47 /*testAll: test LnPD0 LnAge1 LnAge2/ wald lr*/; 48 OUTPUT OUT=QntRegLnAge12 p=predicted; 49 title 'Quantile regression'; 50 RUN; NOTE: The data set WORK.QNTREGEST has 1 observations and 10 variables. NOTE: Compressing data set WORK.QNTREGEST increased size by 100.00 percent. Compressed is 2 pages; un-compressed would require 1 pages. NOTE: The data set WORK.QNTREGLNAGE12 has 26168 observations and 81 variables. NOTE: Compressing data set WORK.QNTREGLNAGE12 decreased size by 82.79 percent. Compressed is 110 pages; un-compressed would require 639 pages. NOTE: PROCEDURE QUANTREG used (Total process time): real time 1.15 seconds cpu time 1.15 seconds 51 52 DATA check; 53 SET QntRegLnAge12; 54 IF AP1 > predicted then Stage = 2; else Stage = 1; 55 dummy = 1; 56 RUN; NOTE: There were 26168 observations read from the data set WORK.QNTREGLNAGE12. NOTE: The data set WORK.CHECK has 26168 observations and 83 variables. NOTE: Compressing data set WORK.CHECK decreased size by 83.05 percent. Compressed is 111 pages; un-compressed would require 655 pages. NOTE: DATA statement used (Total process time): real time 0.25 seconds cpu time 0.25 seconds 57 58 DATA QRParameters; 59 SET check; 60 61 Intercept = -0.6436; /*PROMENA*/ 62 Coeff_LnPD0 = -0.2536; /*PROMENA*/ 63 Coeff_LnAge1 = 0.6548; /*PROMENA*/ 64 Coeff_LnAge2 = 0.2851; /*PROMENA*/ 65 ALPHA = 65 ! SQRT((exp(Intercept+Coeff_LnPD0*log(0.005985769))*0.005985769)*(exp(Intercept+Coeff_LnPD0*log(0.023412558))*0.023412558)) 65 ! ; 66 RUN; NOTE: There were 26168 observations read from the data set WORK.CHECK. NOTE: The data set WORK.QRPARAMETERS has 26168 observations and 88 variables. NOTE: Compressing data set WORK.QRPARAMETERS decreased size by 81.53 percent. Compressed is 121 pages; un-compressed would require 655 pages. NOTE: DATA statement used (Total process time): real time 0.26 seconds cpu time 0.26 seconds 67 68 DATA beta_min_max; 69 SET QRParameters; 3 The SAS System Tuesday, April 28, 2020 12:45:00 PM 70 71 IF (Coeff_LnAge1 > 0 AND Coeff_LnAge2 > 0) THEN DO; 72 MIN = 0; 73 MAX = (1- ALPHA)/LOG(50); 74 END; 75 ELSE IF (Coeff_LnAge1 < 0 AND Coeff_LnAge2 < 0) THEN MAX = 0 AND MIN = -1*(ALPHA)/LOG(50); 76 RUN; NOTE: There were 26168 observations read from the data set WORK.QRPARAMETERS. NOTE: The data set WORK.BETA_MIN_MAX has 26168 observations and 90 variables. NOTE: Compressing data set WORK.BETA_MIN_MAX decreased size by 81.38 percent. Compressed is 119 pages; un-compressed would require 639 pages. NOTE: DATA statement used (Total process time): real time 0.26 seconds cpu time 0.26 seconds 77 78 DATA realized_thresholds/*realized_thresholds_RCG1 realized_thresholds_RCG2*/; 79 SET beta_min_max (KEEP= LnPD0 PDPuncFirstAv RatingClassFirstAV RatingFirstAV RatingCurr RefDate Age ClientID ClientName 79 ! TransID SegmentFinal I1 I2 predicted PDPuncFirstAv PDPuncCurr Intercept Coeff_LnPD0 Coeff_LnAge1 Coeff_LnAge2 ALPHA MIN 79 ! MAX LnAge1 LnAge2); 80 81 PDThreshold = EXP(predicted)*PDPuncFirstAv; /* Predicted PD at reporting date (i.e. PD threshold)*/ 82 IF PDThreshold >= 0.99 THEN PDThreshold = 0.99; 83 /*PDPuncCurr- OBSERVED PD at reporting date*/ 84 IF (PDPuncCurr > PDThreshold) THEN StgChange = 1; /* boolean vector which indicates stage 2 obs. - 1's are in stage 2, 84 ! 0's in stage 1 */ 85 ELSE StgChange = 0; 86 87 *IF I1 = 1 THEN OUTPUT realized_thresholds_RCG1; 88 *ELSE OUTPUT realized_thresholds_RCG2; 89 RUN; NOTE: There were 26168 observations read from the data set WORK.BETA_MIN_MAX. NOTE: The data set WORK.REALIZED_THRESHOLDS has 26168 observations and 26 variables. NOTE: Compressing data set WORK.REALIZED_THRESHOLDS decreased size by 89.51 percent. Compressed is 67 pages; un-compressed would require 639 pages. NOTE: DATA statement used (Total process time): real time 0.18 seconds cpu time 0.17 seconds 90 91 92 PROC SQL; 93 CREATE TABLE obs_stg_change AS 94 SELECT t1.*, sum(t1.StgChange) as sum_StgChange, 95 count(t1.TransID) as no_obs, 96 (SUM(t1.StgChange))/count(t1.TransID) as QUANTILE, 97 (1-(SUM(t1.StgChange))/count(t1.TransID)) AS quantile_or 98 99 FROM realized_thresholds /*realized_thresholds_RCG1 realized_thresholds_RCG2*/ t1; NOTE: The query requires remerging summary statistics back with the original data. NOTE: Compressing data set WORK.OBS_STG_CHANGE decreased size by 88.55 percent. Compressed is 75 pages; un-compressed would require 655 pages. NOTE: Table WORK.OBS_STG_CHANGE created, with 26168 rows and 30 columns. 4 The SAS System Tuesday, April 28, 2020 12:45:00 PM 100 101 QUIT; NOTE: PROCEDURE SQL used (Total process time): real time 0.21 seconds cpu time 0.21 seconds 102 103 104 %macro makro(korak,broj); 105 DATA CTAge; 106 SET obs_stg_change; 107 step=((1-alpha)/log(50))/(50-1); 108 y= alpha+ &korak*log(Age); 109 IF(I1=1) THEN min_Threshold = min(y,PDThreshold); 110 IF(I2=1) THEN max_Threshold = max(y,PDThreshold); 111 min_max_Threshold=COALESCE(min_Threshold, max_Threshold); 112 IF PDPuncCurr > min_max_Threshold THEN StgChange_CT = 1; ELSE StgChange_CT = 0; 113 RUN; 114 115 /*0.5757232999 SUM SQUARE*/ 116 117 PROC SQL; 118 CREATE TABLE CTAge_obs&broj AS 119 SELECT &korak AS step, min(t1.sum_StgChange) as sum_StgChange, 120 (SUM(t1.StgChange_CT)) AS SUM_StgChange_CT, /*a1*//* count # of obs which change stage */ 121 /* (SUM(t1.StgChange_CT) - t1.sum_StgChange) AS PROVERA,*/ 122 /* (SUM(abs(t1.StgChange_CT-t1.StgChange))) AS SUM_StgChange_DIFF,*/ /*Calculate distance between old CT and 122 ! new one */ 123 (SUM((t1.min_max_Threshold-t1.PDThreshold)**2)) AS SSQ2, /*sum of square*/ 124 SQRT(SUM((t1.min_max_Threshold-t1.PDThreshold)**2)) AS dist_prov, /* square root of the distance */ 125 ABS(SUM(t1.StgChange_CT) - min(t1.sum_StgChange)) AS err_stage2, /* Sum of obs which change stage */ 126 ABS(SUM(t1.StgChange_CT)-sum(t1.StgChange))/MIN(t1.no_obs) AS ee 127 FROM WORK.CTAge t1; 128 QUIT; 129 %mend; 130 131 %macro sqlloop(start,end,step,pocetak,kraj); 132 PROC SQL; 133 %DO i=&start. %TO &end. %BY &step.; 134 %DO j=&pocetak. %TO &kraj.; 135 %makro(i,j); 136 %END; 137 %END; 138 QUIT; 139 %mend; 140 141 GOPTIONS NOACCESSIBLE; 142 %LET _CLIENTTASKLABEL=; 143 %LET _CLIENTPROCESSFLOWNAME=; 144 %LET _CLIENTPROJECTPATH=; 145 %LET _CLIENTPROJECTPATHHOST=; 146 %LET _CLIENTPROJECTNAME=; 147 %LET _SASPROGRAMFILE=; 148 %LET _SASPROGRAMFILEHOST=; 149 150 ;*';*";*/;quit;run; 151 ODS _ALL_ CLOSE; 5 The SAS System Tuesday, April 28, 2020 12:45:00 PM 152 153 154 QUIT; RUN; 155 ... 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Thank you, this helps a lot! A what about my first macro? I wrote this : %macro makro(korak,broj); DATA CTAge; SET obs_stg_change; step=((1-alpha)/log(50))/(50-1); y= alpha+ &korak*log(Age); IF(I1=1) THEN min_Threshold = min(y,PDThreshold); IF(I2=1) THEN max_Threshold = max(y,PDThreshold); min_max_Threshold=COALESCE(min_Threshold, max_Threshold); IF PDPuncCurr > min_max_Threshold THEN StgChange_CT = 1; ELSE StgChange_CT = 0; RUN; /*0.5757232999 SUM SQUARE*/ PROC SQL; CREATE TABLE CTAge_obs&broj AS SELECT &korak AS step, min(t1.sum_StgChange) as sum_StgChange, (SUM(t1.StgChange_CT)) AS SUM_StgChange_CT, /*a1*//* count # of obs which change stage */ /* (SUM(t1.StgChange_CT) - t1.sum_StgChange) AS PROVERA,*/ /* (SUM(abs(t1.StgChange_CT-t1.StgChange))) AS SUM_StgChange_DIFF,*/ /*Calculate distance between old CT and new one */ (SUM((t1.min_max_Threshold-t1.PDThreshold)**2)) AS SSQ2, /*sum of square*/ SQRT(SUM((t1.min_max_Threshold-t1.PDThreshold)**2)) AS dist_prov, /* square root of the distance */ ABS(SUM(t1.StgChange_CT) - min(t1.sum_StgChange)) AS err_stage2, /* Sum of obs which change stage */ ABS(SUM(t1.StgChange_CT)-sum(t1.StgChange))/MIN(t1.no_obs) AS ee FROM WORK.CTAge t1; QUIT; %mend; %makro(0,1); %makro(0.001,2); %makro(0.0011,3); %makro(0.0012,4); %makro(0.0013,5); %makro(0.0014,6); ... just like the statement from previous my comment, more that 200 times- that is the number of my steps, that I have to calculate, and I wanted to shorten it with: %macro sqlloop(start,end,step,pocetak,kraj); PROC SQL; %DO i=&start. %TO &end. %BY &step.; %DO j=&pocetak. %TO &kraj.; %makro(i,j); %END; %END; QUIT; %mend; %sqlloop(start=0, end=0.2507245866,step = 0.001,pocetak=1, kraj=242) But it doesn't work. The error was: ERROR: A character operand was found in the %EVAL function or %IF condition where a numeric operand is required. The condition was: &end. ERROR: The %TO value of the %DO I loop is invalid. ERROR: A character operand was found in the %EVAL function or %IF condition where a numeric operand is required. The condition was: &step. ERROR: The %BY value of the %DO I loop is invalid. ERROR: The macro SQLLOOP will stop executing. So, I concluded that there is problem with decimal numbers in macro. Am I right? ... View more

Does it mean that my macro cannot work with decimal numbers?   SInce arguments for my macro are decimal numbers. ... View more

This is the output of the log:   1 The SAS System Tuesday, April 28, 2020 12:45:00 PM 1 ;*';*";*/;quit;run; 2 OPTIONS PAGENO=MIN; 3 %LET _CLIENTTASKLABEL='Program'; 4 %LET _CLIENTPROCESSFLOWNAME='Process Flow'; 5 %LET _CLIENTPROJECTPATH='\\srvnas\common\CRO\Strategic Risk\Credit risk Governance and Models\Credit Risk Modeling\IFRS 5 ! 9\03 IFRS 9 Modeling\02 IFRS 9 TL modeling\QE & TL 2020\Transfer logic\IFRS 9 2020 TL v8.egp'; 6 %LET _CLIENTPROJECTPATHHOST='UCB29756'; 7 %LET _CLIENTPROJECTNAME='IFRS 9 2020 TL v8.egp'; 8 %LET _SASPROGRAMFILE=''; 9 %LET _SASPROGRAMFILEHOST=''; 10 11 ODS _ALL_ CLOSE; 12 OPTIONS DEV=PNG; 13 GOPTIONS XPIXELS=0 YPIXELS=0; 14 FILENAME EGSR TEMP; 15 ODS tagsets.sasreport13(ID=EGSR) FILE=EGSR 16 STYLE=HtmlBlue 17 STYLESHEET=(URL="file:///C:/Program%20Files%20(x86)/SASHome/x86/SASEnterpriseGuide/7.1/Styles/HtmlBlue.css") 18 NOGTITLE 19 NOGFOOTNOTE 20 GPATH=&sasworklocation SYMBOLGEN: Macro variable SASWORKLOCATION resolves to "F:\WORK\_TD4820_SRVSAS06_\Prc2/" 21 ENCODING=UTF8 22 options(rolap="on") 23 ; NOTE: Writing TAGSETS.SASREPORT13(EGSR) Body file: EGSR 24 25 GOPTIONS ACCESSIBLE; 26 options mprint symbolgen mlogic; 27 options compress=binary; 28 29 30 31 DATA QntRegTest; 32 SET QntRegCorp /*QntRegCorpNonRev*/ /*QntRegCorpREV*/; 33 34 /*ISTA JE PODELA KOD SVA 3 segmenta*/ 35 IF RatingClassFirstAv in (1,2,3,4,5) THEN I1 = 1; 36 ELSE I1 = 0; 37 IF RatingClassFirstAv in (6,7,8) THEN I2 = 1; 38 ELSE I2 = 0; 39 40 LnAge1 = LnAge*I1; 41 LnAge2 = LnAge*I2; 42 43 RUN; NOTE: There were 26168 observations read from the data set WORK.QNTREGCORP. NOTE: The data set WORK.QNTREGTEST has 26168 observations and 79 variables. NOTE: Compressing data set WORK.QNTREGTEST decreased size by 83.41 percent. Compressed is 106 pages; un-compressed would require 639 pages. NOTE: DATA statement used (Total process time): real time 0.32 seconds cpu time 0.32 seconds 44 45 PROC QUANTREG DATA=QntRegTest OUTest=QntRegEst algorithm=interior (tolerance=1e-5) ci=resampling; 2 The SAS System Tuesday, April 28, 2020 12:45:00 PM 46 MODELLnAge12WORM: MODEL AP1 = LnPD0 LnAge1 LnAge2 / quantile=0.9 /*plots=all*/; 47 /*testAll: test LnPD0 LnAge1 LnAge2/ wald lr*/; 48 OUTPUT OUT=QntRegLnAge12 p=predicted; 49 title 'Quantile regression'; 50 RUN; NOTE: The data set WORK.QNTREGEST has 1 observations and 10 variables. NOTE: Compressing data set WORK.QNTREGEST increased size by 100.00 percent. Compressed is 2 pages; un-compressed would require 1 pages. NOTE: The data set WORK.QNTREGLNAGE12 has 26168 observations and 81 variables. NOTE: Compressing data set WORK.QNTREGLNAGE12 decreased size by 82.79 percent. Compressed is 110 pages; un-compressed would require 639 pages. NOTE: PROCEDURE QUANTREG used (Total process time): real time 1.12 seconds cpu time 1.15 seconds 51 52 DATA check; 53 SET QntRegLnAge12; 54 IF AP1 > predicted then Stage = 2; else Stage = 1; 55 dummy = 1; 56 RUN; NOTE: There were 26168 observations read from the data set WORK.QNTREGLNAGE12. NOTE: The data set WORK.CHECK has 26168 observations and 83 variables. NOTE: Compressing data set WORK.CHECK decreased size by 83.05 percent. Compressed is 111 pages; un-compressed would require 655 pages. NOTE: DATA statement used (Total process time): real time 0.23 seconds cpu time 0.23 seconds 57 58 DATA QRParameters; 59 SET check; 60 61 Intercept = -0.6436; /*PROMENA*/ 62 Coeff_LnPD0 = -0.2536; /*PROMENA*/ 63 Coeff_LnAge1 = 0.6548; /*PROMENA*/ 64 Coeff_LnAge2 = 0.2851; /*PROMENA*/ 65 ALPHA = 65 ! SQRT((exp(Intercept+Coeff_LnPD0*log(0.005985769))*0.005985769)*(exp(Intercept+Coeff_LnPD0*log(0.023412558))*0.023412558)) 65 ! ; 66 RUN; NOTE: There were 26168 observations read from the data set WORK.CHECK. NOTE: The data set WORK.QRPARAMETERS has 26168 observations and 88 variables. NOTE: Compressing data set WORK.QRPARAMETERS decreased size by 81.53 percent. Compressed is 121 pages; un-compressed would require 655 pages. NOTE: DATA statement used (Total process time): real time 0.29 seconds cpu time 0.29 seconds 67 68 DATA beta_min_max; 69 SET QRParameters; 3 The SAS System Tuesday, April 28, 2020 12:45:00 PM 70 71 IF (Coeff_LnAge1 > 0 AND Coeff_LnAge2 > 0) THEN DO; 72 MIN = 0; 73 MAX = (1- ALPHA)/LOG(50); 74 END; 75 ELSE IF (Coeff_LnAge1 < 0 AND Coeff_LnAge2 < 0) THEN MAX = 0 AND MIN = -1*(ALPHA)/LOG(50); 76 RUN; NOTE: There were 26168 observations read from the data set WORK.QRPARAMETERS. NOTE: The data set WORK.BETA_MIN_MAX has 26168 observations and 90 variables. NOTE: Compressing data set WORK.BETA_MIN_MAX decreased size by 81.38 percent. Compressed is 119 pages; un-compressed would require 639 pages. NOTE: DATA statement used (Total process time): real time 0.29 seconds cpu time 0.29 seconds 77 78 DATA realized_thresholds/*realized_thresholds_RCG1 realized_thresholds_RCG2*/; 79 SET beta_min_max (KEEP= LnPD0 PDPuncFirstAv RatingClassFirstAV RatingFirstAV RatingCurr RefDate Age ClientID ClientName 79 ! TransID SegmentFinal I1 I2 predicted PDPuncFirstAv PDPuncCurr Intercept Coeff_LnPD0 Coeff_LnAge1 Coeff_LnAge2 ALPHA MIN 79 ! MAX LnAge1 LnAge2); 80 81 PDThreshold = EXP(predicted)*PDPuncFirstAv; /* Predicted PD at reporting date (i.e. PD threshold)*/ 82 IF PDThreshold >= 0.99 THEN PDThreshold = 0.99; 83 /*PDPuncCurr- OBSERVED PD at reporting date*/ 84 IF (PDPuncCurr > PDThreshold) THEN StgChange = 1; /* boolean vector which indicates stage 2 obs. - 1's are in stage 2, 84 ! 0's in stage 1 */ 85 ELSE StgChange = 0; 86 87 *IF I1 = 1 THEN OUTPUT realized_thresholds_RCG1; 88 *ELSE OUTPUT realized_thresholds_RCG2; 89 RUN; NOTE: There were 26168 observations read from the data set WORK.BETA_MIN_MAX. NOTE: The data set WORK.REALIZED_THRESHOLDS has 26168 observations and 26 variables. NOTE: Compressing data set WORK.REALIZED_THRESHOLDS decreased size by 89.51 percent. Compressed is 67 pages; un-compressed would require 639 pages. NOTE: DATA statement used (Total process time): real time 0.18 seconds cpu time 0.18 seconds 90 91 92 PROC SQL; 93 CREATE TABLE obs_stg_change AS 94 SELECT t1.*, sum(t1.StgChange) as sum_StgChange, 95 count(t1.TransID) as no_obs, 96 (SUM(t1.StgChange))/count(t1.TransID) as QUANTILE, 97 (1-(SUM(t1.StgChange))/count(t1.TransID)) AS quantile_or 98 99 FROM realized_thresholds /*realized_thresholds_RCG1 realized_thresholds_RCG2*/ t1; NOTE: The query requires remerging summary statistics back with the original data. NOTE: Compressing data set WORK.OBS_STG_CHANGE decreased size by 88.55 percent. Compressed is 75 pages; un-compressed would require 655 pages. NOTE: Table WORK.OBS_STG_CHANGE created, with 26168 rows and 30 columns. 4 The SAS System Tuesday, April 28, 2020 12:45:00 PM 100 101 QUIT; NOTE: PROCEDURE SQL used (Total process time): real time 0.21 seconds cpu time 0.21 seconds 102 103 104 %macro makro(korak,broj); 105 DATA CTAge; 106 SET obs_stg_change; 107 step=((1-alpha)/log(50))/(50-1); 108 y= alpha+ &korak*log(Age); 109 IF(I1=1) THEN min_Threshold = min(y,PDThreshold); 110 IF(I2=1) THEN max_Threshold = max(y,PDThreshold); 111 min_max_Threshold=COALESCE(min_Threshold, max_Threshold); 112 IF PDPuncCurr > min_max_Threshold THEN StgChange_CT = 1; ELSE StgChange_CT = 0; 113 RUN; 114 115 /*0.5757232999 SUM SQUARE*/ 116 117 PROC SQL; 118 CREATE TABLE CTAge_obs&broj AS 119 SELECT &korak AS step, min(t1.sum_StgChange) as sum_StgChange, 120 (SUM(t1.StgChange_CT)) AS SUM_StgChange_CT, /*a1*//* count # of obs which change stage */ 121 /* (SUM(t1.StgChange_CT) - t1.sum_StgChange) AS PROVERA,*/ 122 /* (SUM(abs(t1.StgChange_CT-t1.StgChange))) AS SUM_StgChange_DIFF,*/ /*Calculate distance between old CT and 122 ! new one */ 123 (SUM((t1.min_max_Threshold-t1.PDThreshold)**2)) AS SSQ2, /*sum of square*/ 124 SQRT(SUM((t1.min_max_Threshold-t1.PDThreshold)**2)) AS dist_prov, /* square root of the distance */ 125 ABS(SUM(t1.StgChange_CT) - min(t1.sum_StgChange)) AS err_stage2, /* Sum of obs which change stage */ 126 ABS(SUM(t1.StgChange_CT)-sum(t1.StgChange))/MIN(t1.no_obs) AS ee 127 FROM WORK.CTAge t1; 128 QUIT; 129 %mend; 130 131 %macro sqlloop(start,end,step,pocetak,kraj); 132 PROC SQL; 133 %DO i=&start. %TO &end. %BY &step.; 134 %DO j=&pocetak. %TO &kraj.; 135 %makro(i,j); 136 %END; 137 %END; 138 QUIT; 139 %mend; 140 141 GOPTIONS NOACCESSIBLE; 142 %LET _CLIENTTASKLABEL=; 143 %LET _CLIENTPROCESSFLOWNAME=; 144 %LET _CLIENTPROJECTPATH=; 145 %LET _CLIENTPROJECTPATHHOST=; 146 %LET _CLIENTPROJECTNAME=; 147 %LET _SASPROGRAMFILE=; 148 %LET _SASPROGRAMFILEHOST=; 149 150 ;*';*";*/;quit;run; 151 ODS _ALL_ CLOSE; 5 The SAS System Tuesday, April 28, 2020 12:45:00 PM 152 153 154 QUIT; RUN; 155     It is strange that now there is no error, but no tabels in output of the macro exists. ... 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