In short, I estimate 4 probit regressions, examining the effects of a variable X on the probability of being employment (versus unemployed), on 4 worker groups:
1. total sample
2. sector a
3. sector b
4. sector c.
In each model the command is the same:
dprobit employed X
and all the estimations are successfully carried out.
Then I try to incorporate all 4 regressions in 1, i.e. to estimate a multinomial probit model, instead.
In this case I examine whether variable X affects mobility between these sectors a, b and c, using an indicator variable (id), taking the values 1, 2 and 3, respectively.
The command I use is:
mprobit id X
However, in all my attempts the model cannot be estimated. The results either indicate "Hessian not negatively semi-definite" or the iterations (not concave) just continue forever.
May someone explain to me how this comes, the probit models to be estimated without problems, while the multinomial probit model is not? In the same exactly data set?
Also, any suggestions how to solve this?
1. total sample
2. sector a
3. sector b
4. sector c.
In each model the command is the same:
dprobit employed X
and all the estimations are successfully carried out.
Then I try to incorporate all 4 regressions in 1, i.e. to estimate a multinomial probit model, instead.
In this case I examine whether variable X affects mobility between these sectors a, b and c, using an indicator variable (id), taking the values 1, 2 and 3, respectively.
The command I use is:
mprobit id X
However, in all my attempts the model cannot be estimated. The results either indicate "Hessian not negatively semi-definite" or the iterations (not concave) just continue forever.
May someone explain to me how this comes, the probit models to be estimated without problems, while the multinomial probit model is not? In the same exactly data set?
Also, any suggestions how to solve this?
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