Dear statalist
I´m trying to fit a discrete-time hazard model using a cloglog-link:
xi: xtcloglog y Dyear6 Dyear8 i.age*t marital urban children if gender==1
y: a binary outcome (0 or 1)
Dyear6 (7): Dummy for year 6 and 7
i.age (50 dummies for age)
t: chronological time (12 years)
demographics are dummies (0 or 1)
The dataset is large (1.5 millions observations of which only 3600 are positive outcomes).
So I´m modeling a rare outcome with individual unbalanced panel data.
Is there something in this model that makes ML estimation impossible?
I´ve tried running the model with agegroups but always get the „not concave“ message in the iteration process.
Much appreciated if someone could be of assistance on this question.
Thorhildur
I´m trying to fit a discrete-time hazard model using a cloglog-link:
xi: xtcloglog y Dyear6 Dyear8 i.age*t marital urban children if gender==1
y: a binary outcome (0 or 1)
Dyear6 (7): Dummy for year 6 and 7
i.age (50 dummies for age)
t: chronological time (12 years)
demographics are dummies (0 or 1)
The dataset is large (1.5 millions observations of which only 3600 are positive outcomes).
So I´m modeling a rare outcome with individual unbalanced panel data.
Is there something in this model that makes ML estimation impossible?
I´ve tried running the model with agegroups but always get the „not concave“ message in the iteration process.
Much appreciated if someone could be of assistance on this question.
Thorhildur
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