Hello
I estimate democratization in the European Union's neighborhood as a
function of EU incentives (e.g. whether the EU proposes the chance to get a
member) and some controls. Since democracy (measured by Freedom House) is
stepwise and censored, I use a multinomial probit; since I have panel data
(36 countries and 13 years; for some missings altogether 385 observations),
I need a random effects model. The model includes a time trend.
The syntax applied is
gllamm [dependent] year [indepvars], link(oprobit) i([Count variable over
countries]) (1)
After estimating some models, I applied some linear transformations to the
variables, for example
gen yeart = year - 1990
gllamm [dependent] yeart [indepvars], link(oprobit) i([Count variable over
countries]) (2)
Much to my surprise I found that the results of (2) deviate from those of
(1) - not only in the coefficient of yeart vs year and the constant, as I
expected, but in every coefficient and z stats.
Can someone explain me why this is the case? Of course, I studied
Rabe-Hesketh/Skrondal (2005) on the subject, but did not find anything
helpful. Have I overread or misunderstood something? Or is there any
solution I simply do not see?
Thanks for all help!
Hanno
Rabe-Hesketh, Sophia, and Anders Skrondal (2005): Multilevel and
Longitudinal Modeling Using Stata. College Station: Stata Press
--
Dr. Hanno Scholtz <[email protected]>
University of Zurich, Sociological Institute
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