Hello,
I am trying to use Multinomail probit model for a binary variable (had treatment or not). Unluckily the estimated parameters are not significant and the constant and the Chi2 are not significant too.
Kindly the results are displayed below, Please do have a look and your suggestion is much welcomed.
One more question, I am using Stata version 15. I am conducting Heckprobit model where I want to calculate miles ratio to use it as independent variable in other regression model. Any help with this would be highly appreciated.
My syntax:
--------------
mprobit no_treatment s614a female two_child many_child agegrp2 agegrp3 agegrp4 agegrp5 agegrp6 agegrp7 priedu secedu hgedu fpriedu fsecedu fhgedu wipoorer wimiddle wiricher wirichest earning v714 fprofessionls fagr_service fmanual insured v481a v481b v481c urban
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Code and Results:
[. mprobit no_treatment s614a female two_child many_child ///
> agegrp2 agegrp3 agegrp4 agegrp5 agegrp6 agegrp7 ///
> priedu secedu hgedu fpriedu fsecedu fhgedu ///
> wipoorer wimiddle wiricher wirichest earning v714 ///
> fprofessionls fagr_service fmanual ///
> insured v481a v481b v481c urban
Iteration 0: log likelihood = -132.71612
Iteration 1: log likelihood = -132.46317
Iteration 2: log likelihood = -132.46279
Iteration 3: log likelihood = -132.46279
Multinomial probit regression Number of obs = 249
Wald chi2(30) = 37.99
Log likelihood = -132.46279
Prob > chi2 = 0.1499
I am trying to use Multinomail probit model for a binary variable (had treatment or not). Unluckily the estimated parameters are not significant and the constant and the Chi2 are not significant too.
Kindly the results are displayed below, Please do have a look and your suggestion is much welcomed.
One more question, I am using Stata version 15. I am conducting Heckprobit model where I want to calculate miles ratio to use it as independent variable in other regression model. Any help with this would be highly appreciated.
My syntax:
--------------
mprobit no_treatment s614a female two_child many_child agegrp2 agegrp3 agegrp4 agegrp5 agegrp6 agegrp7 priedu secedu hgedu fpriedu fsecedu fhgedu wipoorer wimiddle wiricher wirichest earning v714 fprofessionls fagr_service fmanual insured v481a v481b v481c urban
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Code and Results:
[. mprobit no_treatment s614a female two_child many_child ///
> agegrp2 agegrp3 agegrp4 agegrp5 agegrp6 agegrp7 ///
> priedu secedu hgedu fpriedu fsecedu fhgedu ///
> wipoorer wimiddle wiricher wirichest earning v714 ///
> fprofessionls fagr_service fmanual ///
> insured v481a v481b v481c urban
Iteration 0: log likelihood = -132.71612
Iteration 1: log likelihood = -132.46317
Iteration 2: log likelihood = -132.46279
Iteration 3: log likelihood = -132.46279
Multinomial probit regression Number of obs = 249
Wald chi2(30) = 37.99
Log likelihood = -132.46279
Prob > chi2 = 0.1499
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