cancel
Turn on suggestions
Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.
Showing results for 
Search instead for 
Did you mean: 
Kyungjae
‎04-25-2022
  • 11 Posts
  • 5 Likes Given
  • 0 Solutions
  • 1 Likes Received

Re: Multinomial Probit Predicted Value

by Fluorite | Level 6 in SAS Forecasting and Econometrics
‎04-20-2022 10:43 AM
1 Like
‎04-20-2022 10:43 AM
1 Like
I really appreciate your help. Now I understand that it is nearly impossible to calculate the predicted value by myself. Thank you! ... View more

Re: Multinomial Probit Predicted Value

by Fluorite | Level 6 in SAS Forecasting and Econometrics
‎04-20-2022 12:17 AM
‎04-20-2022 12:17 AM
Thank you for your answer. I read the papers you recommended but I could not fully understand. Thus, I ask another question.   We can easily calculate predicted(or expected) value(probability of choosing alternative j) when we use a multinomial logit model. The equation is as following. Then I wonder whether the following equation is suitable for calculating the predicted value(probability of choosing alternative j) when using a multinomial probit model. I really appreciate for your help.   ... View more

Multinomial Probit Predicted Value

by Fluorite | Level 6 in SAS Forecasting and Econometrics
‎04-19-2022 04:21 AM
‎04-19-2022 04:21 AM
Hi, I am Kyungjae Lee. I analyzed a multinomial probit model using Proc MDC. My model is as of mode choice. There are four categories: bus train multi car(ref.). My code is as following:   proc mdc data=con05 outest=aaa covout; where area1=11 and c_area1=11; model decision= bus train multi age_bus gender_bus marry_bus school2_bus school3_bus status2_bus status3_bus own_bus htype2_bus htype3_bus car_own_bus age_train gender_train marry_train school2_train school3_train status2_train status3_train own_train htype2_train htype3_train car_own_train age_multi gender_multi marry_multi school2_multi school3_multi status2_multi status3_multi own_multi htype2_multi htype3_multi car_own_multi / nchoice=4 type=mprobit maxiter=10000000 unitvariance=(1 2 3 4); id subject; output out=abc pred=p xbeta=xbr; run;   I could find the predicted value by inputting this code: output out=abc pred=p. However, I want to know how to calculate the predicted value. I tried to find out an equation to calculate it, but I could not find it. I will really appreciate if you let me know the exact equation.   Thank you.   ... View more

Re: Proc MDC "At least one element of the gradient is greater than 1e-...

by Fluorite | Level 6 in SAS Forecasting and Econometrics
‎02-04-2022 07:43 AM
‎02-04-2022 07:43 AM
These are the entire codes I ran. Multinomial Logit model works very well.   libname lkj 'f:/01. korea census/2015_2p'; libname a 'f:/저출산/random coefficient';   data birth2015; set lkj.h2015; id=c1; area=c2*1000+c3; area1=c2; if area1=11; age=c8; if c8 ge 51 and c8 le 60; *31-40 years old; if c7=2; *female only; if c69=99 then c69=0; if c70=99 then c70=0; birth=c69+c70; *# of birth; if birth=. then birth=0; ac1=0; ac2=0; if c8 ge 51 and c8 le 55 then ac1=1; if c8 ge 56 and c8 le 60 then ac2=1; marry=0; if c67=2 then marry=1; *married; mig1=0; mig2=0; mig3=0; if c19=2 then mig1=1; *non-migrant; if c19=3 then mig2=1; *migrant from same city; if c19=4 and area1=c20 then mig2=1; * migrant from same province; if c19=4 and area1 ne c20 then mig3=1; *migrant from other province; if c19=5 then delete; *delete migrant from other countries; if mig1=1 or mig2=1 then mig=0; if mig3=1 then mig=1; emp=0; if c61 in (1 2 3) then emp=1; *employed; edu=0; if c13 in (6) and c14=1 then edu=1; if c13 in ( 7 8 ) then edu=1;*more than bachelor age_sq=age*age; if c15=4 then delete; *delete observations who immigrated from other countries; honam=0; if c16 in (24 35 36) then honam=1; htype=0; if htype2=1 then htype=1; *live in apartment if own=. then delete; n_birth=.; if birth=0 then n_birth=3; *ref.; if birth=1 then n_birth=1; if birth ge 2 then n_birth=2; keep age age_sq area1 area birth ac1 ac2 marry mig1 mig2 mig3 mig own emp edu htype h_age  n_birth c16 honam; run; data b2015; set birth2015; subject=_n_; do i=1 to 3; decision=(n_birth eq i); one=(i eq 1); two=(i eq 2); one_age=one*age; one_age_sq=one*age_sq; one_ac1=one*ac1; one_ac2=one*ac2; one_edu=one*edu; one_emp=one*emp; one_own=one*own; one_htype=one*htype; one_h_age=one*h_age; one_honam=one*honam; two_age=two*age; two_age_sq=two*age_sq; two_ac1=two*ac1; two_ac2=two*ac2; two_edu=two*edu; two_emp=two*emp; two_own=two*own; two_htype=two*htype; two_h_age=two*h_age; two_honam=two*honam; output; end; run; /*Multinomial Probit Model*/ proc mdc data=b2015; model decision=one one_ac2 one_edu one_own one_honam two two_ac2 two_edu two_own two_honam /type=mprobit nchoice=3 maxiter=1000000; id subject; nloptions gconv2=0 maxfunc=1000000; run;   These are all the notes that I got.   NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: At least one element of the gradient is greater than 1e-3. NOTE: 프로시저 MDC 실행(총 프로세스 시간): 실행 시간 38.64 초 cpu 시간 38.60 초   Thank you. ... View more

Re: Proc MDC "At least one element of the gradient is greater than 1e-...

by Fluorite | Level 6 in SAS Forecasting and Econometrics
‎02-04-2022 05:56 AM
‎02-04-2022 05:56 AM
This is the code I run.   proc mdc data=b2015;  model decision=one one_ac2 one_edu one_own one_honam  two two_ac2 two_edu two_own two_honam  /type=mprobit  nchoice=3  id subject;  run;   And these are the notes I get.   NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: At least one element of the gradient is greater than 1e-3. Thank you.   ... View more

Re: Proc MDC "At least one element of the gradient is greater than 1e-...

by Fluorite | Level 6 in SAS Forecasting and Econometrics
‎02-04-2022 12:25 AM
‎02-04-2022 12:25 AM
There is another note that shows up together: Convergence criterion (GCONV=1E-8) satisfied.  I think optimization worked properly.   All the independent variables are dummy variables, so I used Class statement but it does not work well.   Compared to multinomial logit result, the signs of estimates are same but the statistical significance are different. The estimates of multinomial probit model are less significant. I used many nloptions as you recommended but the note "At least one element of the gradient is greater than 1e-3" keeps appearing.   The multinomial probit result is as follows.   May I ask another solution?  ... View more

Proc MDC "At least one element of the gradient is greater than 1e-3"

by Fluorite | Level 6 in SAS Forecasting and Econometrics
‎02-03-2022 06:05 AM
‎02-03-2022 06:05 AM
Hi, I am running multinomial probit model using proc mdc. It gives me the note: At least one element of the gradient is greater than 1e-3.  Can anybody help me how to solve this problem?   This is the code.   proc mdc data=b2015; model decision=one one_ac2 one_edu one_own one_honam two two_ac2 two_edu two_own two_honam /type=mprobit nchoice=3; id subject; run;   The dependent  variable denotes the number of birth(0, 1, or ge than 2). The reference group is 0 birth. When I use 'clogit' instead of 'mprobit' for type, it gives the exact same results with the multinomial logit model. And there is no note. So I think there is no problem within the data. I used diverse options but I could not solve the problem. I hope someone can help me.   Thanks. Kyungjae ... View more

Re: Multinomial Probit

by Fluorite | Level 6 in SAS Programming
‎01-04-2022 08:42 AM
‎01-04-2022 08:42 AM
Thank you for your advice, but cumulative probit shows the result of Ordered Probit Model. I tried several procedures which involve cumulative probit as a link function including Proc Genmod and Proc Gee, and none of them showed the result of Multinomial Probit Model. ... View more

Multinomial Probit

by Fluorite | Level 6 in SAS Programming
‎01-04-2022 01:37 AM
‎01-04-2022 01:37 AM
Hello, I am using SAS 9.4 to estimate multinomial probit model. The dependent variable has the values 1, 2, 3, 4 and there are about 10 (binary and continuous) independent variables. However, I could not find the proper SAS Procedure for multinomial probit regression except Proc MDC. Is there any procedure for multinomial probit model?   Thanks ... View more

proc spatialreg optimization

by Fluorite | Level 6 in Statistical Procedures
‎10-14-2020 01:49 AM
‎10-14-2020 01:49 AM
I used proc spatialreg to analyze spatial econometrics model. However, error messages kept showing up depending on model or data. "Opimization failed. Interpret the estimates with care" or  "Optimization cannot improve the function value". In addition, although the message 'Algorithm converged' comes out, standard error, t value, and p value don`t appear.  I specified quasi-Newton method and I specified maximum number of iterations as 1000.   When I use SAR model, this output comes out. Model Fit Summary         Dependent Variable t_crime         Number of Observations 116         Data Set LKJ.D05_F         Spatial Weights WORK.SWM         Model SAR         Log Likelihood -1058         Maximum Absolute Gradient 1.85E-06         Number of Iterations 167         Optimization Method Quasi-Newton         AIC 2144         SBC 2182                     Algorithm converged.                       Parameter Estimates Parameter DF Estimate Standard t Value Approx Error Pr > |t| Intercept 1 8601.954 6.352752 1354.05 <.0001 pop 1 29.05341 2.875343 10.1 <.0001 f_rate 1 794.7308 387.8651 2.05 0.0405 ac 1 -634.92 147.059 -4.32 <.0001 ad 1 380.3811 100.034 3.8 0.0001 old 1 -480.73 110.843 -4.34 <.0001 female 1 179.7138 108.2588 1.66 0.0969 college 1 -164.403 58.18005 -2.83 0.0047 mig 1 -127.122 44.54226 -2.85 0.0043 house_1 1 167.0526 66.63577 2.51 0.0122 atax 1 315.4854 81.58125 3.87 0.0001 train 1 1368.529 841.2449 1.63 0.1038 _rho 1 0.088868 0.124355 0.71 0.4748 _sigma2 0 4888967 . . .   However, when I use SAC model and other models, these output come out. Model Fit Summary         Dependent Variable t_crime         Number of Observations 116         Data Set LKJ.D05_F         Spatial Weights WORK.SWM         Model SAC         Log Likelihood -1474         Maximum Absolute Gradient 2824         Number of Iterations 363         Optimization Method Quasi-Newton         AIC 2977         SBC 3018                     ERROR: Optimization failed. Interpret the estimates with care.                       Parameter Estimates Parameter DF Estimate Standard t Value Approx Error Pr > |t| Intercept 0 7284.729 . . . pop 1 27.55415 0.928053 29.69 <.0001 f_rate 1 315.0701 126.6845 2.49 0.0129 ac 1 -540.133 56.69547 -9.53 <.0001 ad 1 259.395 39.98755 6.49 <.0001 old 1 -450.053 36.2234 -12.42 <.0001 female 1 48.66603 79.86923 0.61 0.5423 college 1 -161.944 19.16263 -8.45 <.0001 mig 1 -92.4311 14.38729 -6.42 <.0001 house_1 1 148.8553 22.76375 6.54 <.0001 atax 1 307.1371 26.51714 11.58 <.0001 train 1 1597.459 270.7654 5.9 <.0001 _rho 1 -0.6494 0.117049 -5.55 <.0001 _lambda 0 0.999642 . . . _sigma2 1 499441 0.128961 3872796 <.0001   or  Model Fit Summary         Dependent Variable t_crime         Number of Observations 116         Data Set LKJ.D05_F         Spatial Weights WORK.SWM         Model SMA         Log Likelihood -1053         Maximum Absolute Gradient 1223         Number of Iterations 281         Optimization Method Quasi-Newton         AIC 2133         SBC 2169                     WARNING: Optimization cannot improve the function value.                       Parameter Estimates Parameter DF Estimate Standard t Value Approx Error Pr > |t| Intercept 1 -3562.56 43.40649 -82.07 <.0001 pop 1 31.24189 0.270455 115.52 <.0001 f_rate 0 1113.872 . . . ac 0 -631.177 . . . ad 0 459.1431 . . . old 0 -435.751 . . . female 1 352.0277 0.973919 361.45 <.0001 college 0 -153.23 . . . mig 0 -106.113 . . . house_1 1 193.9406 1.662491 116.66 <.0001 atax 1 339.6874 13.22217 25.69 <.0001 train 1 1067.575 87.99192 12.13 <.0001 _lambda 0 1 . . . _sigma2 0 2669892 . . . Restrict1 -1 94906303 . . . *   I wonder how I could solve this optimization problem. Thank you. ... View more

proc spatialreg optimization failure

by Fluorite | Level 6 in Statistical Procedures
‎10-13-2020 06:42 AM
‎10-13-2020 06:42 AM
I used proc spatialreg to analyze spatial econometrics model. However, error messages kept showing up depending on model or data. "Opimization failed. Interpret the estimates with care" or  "Optimization cannot improve the function value". In addition, although the message 'Algorithm converged' comes out, standard error, t value, and p value don`t appear.  I specified quasi-Newton method and I specified maximum number of iterations as 1000.   Algorithm converged. Parameter EstimatesParameter DF Estimate StandardError t Value ApproxPr > |t|Interceptpopf_rateacadoldfemalecollegemighouse_1ataxtrain_lambda_sigma2Restrict1 1 12675 148.614569 85.29 <.0001 1 32.448024 0.170667 190.12 <.0001 1 505.773196 139.973396 3.61 0.0003 1 -585.435224 12.982785 -45.09 <.0001 1 214.707052 12.250951 17.53 <.0001 1 -499.383547 12.682656 -39.38 <.0001 1 214.837050 1.364687 157.43 <.0001 1 -240.843630 0.983443 -244.90 <.0001 0 -199.938586 . . . 0 176.465831 . . . 1 98.594504 3.551837 27.76 <.0001 1 1629.535839 152.198891 10.71 <.0001 0 1.000000 . . . 1 11625708 0.255397 4.552E7 <.0001 -1 94905077 . . . *   ERROR: Optimization failed. Interpret the estimates with care. Parameter EstimatesParameter DF Estimate StandardError t Value ApproxPr > |t|Interceptpopf_rateacadoldfemalecollegemighouse_1ataxtrain_rho_lambda_sigma2 1 4877.620429 5802.227026 0.84 0.4005 1 27.446622 1.054131 26.04 <.0001 1 -738.228100 161.714703 -4.57 <.0001 1 -460.278202 49.892925 -9.23 <.0001 1 317.332993 23.506006 13.50 <.0001 1 -514.798669 37.320073 -13.79 <.0001 1 134.349259 93.345199 1.44 0.1501 1 -192.431771 19.548520 -9.84 <.0001 1 -172.167729 18.373370 -9.37 <.0001 1 221.460165 26.588296 8.33 <.0001 1 383.475678 26.552452 14.44 <.0001 1 1420.679084 299.021935 4.75 <.0001 0 0.377923 . . . 0 -3.805885 . . . 1 660740 74.384999 8882.71 <.0001   WARNING: Optimization cannot improve the function value. Parameter EstimatesParameter DF Estimate StandardError t Value ApproxPr > |t|Interceptpopf_rateacadoldfemalecollegemighouse_1ataxtrain_lambda_sigma2Restrict1 0 5033.903709 . . . 1 31.303281 0.028436 1100.83 <.0001 1 569.039293 43.805973 12.99 <.0001 0 -527.967647 . . . 0 438.671229 . . . 0 -363.941741 . . . 0 84.402772 . . . 0 -110.553241 . . . 0 -86.368103 . . . 1 223.582940 2.153441 103.83 <.0001 0 381.665965 . . . 0 790.001959 . . . 0 1.000000 . . . 0 3031879 . . . -1 81288778 . . . * ... View more
Contact Me
Online Status
Offline
Date Last Visited
‎04-25-2022 04:54 AM
Likes given to
Likes from
View All
Quick links