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st: From: Jean-François Houde <houdejf@qed.econ.queensu.ca> |

Date |
Wed, 4 May 2005 14:03:28 -0400 |

Hi,

I'm trying to use GLLAMM to estimate an ordered probit model on a database with

two levels (i.e. survey of 16 activities in 196 firms). The linear component with (3)

random effects is as follow:

y_ij=X_ijB + W_ij \theta_i + \epsilon_ij,

where \epsilon is a standard normal variable, and \theta_i is a 3X1 vector distributed

normally accross firms (indexed by i), with mean 0 and covariance matrix \Sigma (I

want to estimate the correlation betwen the \theta's).

I tried to estimate this model using the following command:

eq: var1=w1;

eq: var2=w2;

eq: var3=w3;

gllamm y X, link(oprobit) fam(gaussian) eqs(var1 var2 var3) nrf(3) adapt;

However the program failled to complete the first iteration and reported the following error message:

could not calculate numerical derivatives

flat or discontinuous region encountered

(error occurred in ML computation)

(use trace option and check correctness of initial model)

I tried to run the same model using the binomial probit link (i.e. collapsing the ordered choice in two choices), and it worked perfectly. I have also noticed in the GLLAMM manual that the ordered response examples are always estimated

with the binomial family (i.e. distribution assumption for the random effects). I tried to use the binomial family instead, but it

wouldn't let me estimate three correlated random effects. Also, I tried the example from the GLLAMM manual with the gaussian family instead of the binomial family, and stata reported the same error message (which indicates that my data-set is not the source of the problem).

Does anybody have any idea how to estimate this model in GLLAMM ?

Thank you very much in advance,

J-F

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