It's possible that as it is implemented, the bprobit in R does not take an endogenous
variable...
Best,
Kosuke
Department of Politics
Princeton University
http://imai.princeton.edu
On Sep 20, 2011, at 2:06 AM, Stefanie Schurer wrote:
Hi Kosuke,
I am a problem with Zelig’s bivariate probit model. I am trying to replicate a study by
Carrasco 2001, JBES, which estimates jointly labour supply and fertility with a bivariate
probit model. I can replicate her binary response results with R, but once using zelig’s
- bprobit – command I get 14 error messages and totally nonsensical results. The problem
is that when estimating the exact same model with STATA’s – biprobit -- command, I am able
to replicate Carrasco’s results. Is there any known bug in bprobit which I happen to be
not aware of?
This is what I programmed (please note that this is a recursive model in which f =
fertility is an endogenous RHS variable, and thus is separately modelled in the second
equation, using an instrument for identification “dsex”).
Any help would be highly appreciated as I intend to teach this to my third year
econometrics students next Thursday.
Cheers,
Stefi
######
fml <- list(mu1 = dhw ~ f + ags26l + fxag26l + educ2 + educ3 + drace + age + income +
dhwl, mu2 = f ~ ags26l + educ2 + educ3 + drace + age + income + dsex)
z.out <- zelig(fml, model = "blogit", data = mydata)
z.out
Below is the errors I get
Warning messages:
1: glm.fit: algorithm did not converge
2: In checkwz(wz, M = M, trace = trace, wzeps = control$wzepsilon) :
805 elements replaced by 1.819e-12
3: In tfun(mu = mu, y = y, w = w, res = FALSE, eta = eta, ... :
fitted values close to 0 or 1
4: In checkwz(wz, M = M, trace = trace, wzeps = control$wzepsilon) :
2064 elements replaced by 1.819e-12
5: In tfun(mu = mu, y = y, w = w, res = FALSE, eta = eta, ... :
fitted values close to 0 or 1
6: In tfun(mu = mu, y = y, w = w, res = FALSE, eta = eta, ... :
fitted values close to 0 or 1
7: In tfun(mu = mu, y = y, w = w, res = FALSE, eta = eta, ... :
fitted values close to 0 or 1
8: In tfun(mu = mu, y = y, w = w, res = FALSE, eta = eta, ... :
fitted values close to 0 or 1
9: In tfun(mu = mu, y = y, w = w, res = FALSE, eta = eta, ... :
fitted values close to 0 or 1
10: In tfun(mu = mu, y = y, w = w, res = FALSE, eta = eta, ... :
fitted values close to 0 or 1
11: In tfun(mu = mu, y = y, w = w, res = FALSE, eta = eta, ... :
fitted values close to 0 or 1
12: In tfun(mu = mu, y = y, w = w, res = FALSE, eta = eta, ... :
fitted values close to 0 or 1
13: In tfun(mu = mu, y = y, w = w, res = FALSE, eta = eta, ... :
fitted values close to 0 or 1
14: In eval(expr, envir, enclos) :
iterations terminated because half-step sizes are very small
-
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