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Re: st: d0 and Log-logistic hazard
Alfonso Miranda <email@example.com> asks:
> I am trying to write a d0 maximum likelihood code for estimating a
> split-population duration model with gamma unobserved individual
> heterogeneity and log-logistic hazard. The estimation strategy finds first
> initial values from streg and set the initial probability of never fail to
> 2%. Then the ml code is used. I already checked that my log-likelihood is
> correctly written and the ml check is ok. But still Stata finds that the
> log-likelihood function is not concave and does not converges. Having this
> in mid, and in the spirit to find my potential mistake, I tried to create a
> d0 code for the standard duration model with gamma unobserved individual
> heterogeneity from lloghet_glf. Basically I did no changes to the code but
> take away the part that refers to d1 and d2 methods. Running this
> alternative program I learned that Stata does the same: it keeps saying that
> the log-likelihood function is not concave and it does not converges. Hence,
> it seems that the problem is not in the d0 code. Why is this is happening?
> Is it that a d1 (or maybe a d2) method is required whenever a log-logistic
> model is to be estimated?
> Could you give me some hint on what further action should I take?
[contents of .do file omitted]
You were close. In your do file (which I do not list in order to keep
this brief) you had the lines:
local t = _t
local d = _d
You should change these to
local t _t
local d _d
and everything will work just fine. The difference between the two contructs
is that the first envokes Stata's expression evaluator, which evaluates "_t"
to be the value of the first observation of the variable _t, which in effect
sets the local t to be a constant.
The second construct is what you want. By not using the equal sign, you are
performing literal string substitution, which will set the local macro t to
contain the string "_t". That is, the macro t will always refer to the
variable _t in your data, as it is supposed to when you want the refer to part
of the response in a survival model.
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