Dear Statalist,
I am using a panel dataset with 993 observations and 199 ids. The regression I want to run contains a binary dependent variable and a binary endogenous variable. Trying to find an adequate model, I searched in Statalist and found several useful advices:
1. As proposed by Wooldridge: "... with a binary y1 and binary y2, you should use two methods. (1) A standard linear model estimated by 2SLS. This is what Angrist and Pischke propose in "Mostly Harmless Econometrics." (2) Use the so-called "biprobit" model, where y1 and y2 are modeled as probits. This is a joint maximum likelihood procedure. You should compute the average marginal effect from the biprobit and compare it with the 2SLS estimate." (https://www.statalist.org/forums/for...ndent-variable)
2. Searching for a solution to use a bivariate model in a fixed effects panel, I found an advice by Alfonso Sánchez-Peñalver. "For a fixed-effects estimation I'm not sure that with binary dependent variables demeaning the variables by group ... would work, I actually think it won't, so the only suggestion that pops into mind is to create the dummy (binary) variables for ... [id] and run the model with the dummy variables. It may not converge easily, depending on the number of insurees you have in your data, i.e. the number of dummies you would have to include. You can use the regular bivariate probit estimation you did including the dummy variables for this, or cmp as well. They would both work." (https://www.statalist.org/forums/for...variate-probit)
I followed these steps and my bivariate model
did not converge.
This is different for the cmp command:
It converges but does not compute all SEs and shows "Warning: regressor matrix for I equation appears ill-conditioned. (Condition number = 1674.5519.)"
Yet, when writing
it converges, but I guess this is the wrong command.
Do you have any ideas how to sucessfull estimate the bivariate model with a fixed effects panel?
Thanks a lot!
I am using a panel dataset with 993 observations and 199 ids. The regression I want to run contains a binary dependent variable and a binary endogenous variable. Trying to find an adequate model, I searched in Statalist and found several useful advices:
1. As proposed by Wooldridge: "... with a binary y1 and binary y2, you should use two methods. (1) A standard linear model estimated by 2SLS. This is what Angrist and Pischke propose in "Mostly Harmless Econometrics." (2) Use the so-called "biprobit" model, where y1 and y2 are modeled as probits. This is a joint maximum likelihood procedure. You should compute the average marginal effect from the biprobit and compare it with the 2SLS estimate." (https://www.statalist.org/forums/for...ndent-variable)
2. Searching for a solution to use a bivariate model in a fixed effects panel, I found an advice by Alfonso Sánchez-Peñalver. "For a fixed-effects estimation I'm not sure that with binary dependent variables demeaning the variables by group ... would work, I actually think it won't, so the only suggestion that pops into mind is to create the dummy (binary) variables for ... [id] and run the model with the dummy variables. It may not converge easily, depending on the number of insurees you have in your data, i.e. the number of dummies you would have to include. You can use the regular bivariate probit estimation you did including the dummy variables for this, or cmp as well. They would both work." (https://www.statalist.org/forums/for...variate-probit)
I followed these steps and my bivariate model
Code:
biprobit (Y1 Z P order $x hhid_*) (Y2 Y1 P order $x hhid_*), vce(cluster hhid)
This is different for the cmp command:
Code:
cmp (Y2= Y1 P order $x hhid_*) (Y1 = Z P order $x hhid_*), ind($cmp_probit $cmp_probit ) cluster(hhid) nonrtolerance
Yet, when writing
Code:
cmp (Y1 = Z P order $x hhid_*) (Y2= Y1 P order $x hhid_*), ind($cmp_probit $cmp_probit ) cluster(hhid)
Do you have any ideas how to sucessfull estimate the bivariate model with a fixed effects panel?
Thanks a lot!
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