Google's AI is claiming that there is a het option in gsem. But, I am 99.99999% sure it is hallucinating because I cannot find this option in the documentation anywhere, nor can I get an example to work. If there is a way to estimate a heteroscedastic probit or oprobit model in gsem, I would love to know about it. (I do know about commands like hetprobit, hetoprobit, and oglm.) Here is what Google Chrome's AI says:
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To estimate a heteroskedastic probit model using gsem in Stata, you would need to specify the "het()" option within the gsem command, providing the variable that explains the heteroskedasticity as an argument; essentially, you're telling Stata to allow the error variance to differ based on the values of that specified variable.
Example Stata code:
gsem (binary_outcome <- independent_variable1 independent_variable2, probit) het(heteroskedasticity_variable)
Explanation:
. webuse nhanes2f, clear
. gsem (diabetes <- female black height weight, probit) het(female)
variable het not found;
Perhaps you meant 'het' to specify a latent variable.
For 'het' to be a valid latent variable specification, 'het' must begin with a capital letter.
r(111);
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AI answers can be handy when they are accurate. But, I never trust anything AI says until I can verify.
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To estimate a heteroskedastic probit model using gsem in Stata, you would need to specify the "het()" option within the gsem command, providing the variable that explains the heteroskedasticity as an argument; essentially, you're telling Stata to allow the error variance to differ based on the values of that specified variable.
Example Stata code:
gsem (binary_outcome <- independent_variable1 independent_variable2, probit) het(heteroskedasticity_variable)
Explanation:
- gsem:
This is the command to run a generalized structural equation model, which allows for more complex model specifications including binary outcomes like in a probit model. - (binary_outcome <- independent_variable1 independent_variable2, probit):
This part defines the binary outcome variable ("binary_outcome") and its relationship with the independent variables ("independent_variable1" and "independent_variable2") using a probit link function. - het(heteroskedasticity_variable):
This is the key part for specifying heteroskedasticity; it indicates that the variance of the error term will be allowed to vary depending on the values of the variable "heteroskedasticity_variable".
. webuse nhanes2f, clear
. gsem (diabetes <- female black height weight, probit) het(female)
variable het not found;
Perhaps you meant 'het' to specify a latent variable.
For 'het' to be a valid latent variable specification, 'het' must begin with a capital letter.
r(111);
-------
AI answers can be handy when they are accurate. But, I never trust anything AI says until I can verify.
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