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"... or can I still use the fixed/random effect models?"
They need not be mutually exclusive as I understand them. - xtpoisson- and
-xtnbreg-, for instance, do fit RE and FE models. You may also want to look
at -xtmepoisson-
HTH
Martin
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Von: [email protected]
[mailto:[email protected]] Im Auftrag von
[email protected]
Gesendet: Donnerstag, 5. März 2009 07:51
An: [email protected]
Betreff: st: appropriate techniques for analysing suicide mortality data-
xtreg fe? poisson?
Dear statalist,
This is a question regarding the appropriateness of the techniques I have
chosen for my phd. Unfortunately, I do not have a statistician at my uni who
can provide me with an expert opinion on this- hence why I am posting this
message on stata list. I am sorry for the list of questions that follow, but
I would be truly grateful if any one can offer me advise.
I have panel data containing suicide rates for 74 countries over a long time
series. I wish to test the relationship that these have with social
variables within the country (ie. Unemployment rate etc.,).
Suicide rates are transformed using the square root transformation and
social data is transformed using the ln transformation.
Currently I have been using fixed/random effects to estimate the
relationship between these variables. I have also made a lagged model of
these relationships using xtabond2.
However, I realise that researchers often use the poisson distribution in
conducting analysis with suicide data. Considering this, should I stick to
techniques that use this distribution (xtgee is one I know? I am not sure
about any others that may also use this)? or can I still use the
fixed/random effect models? Also, if I do need to use the poisson
distribution, how can I conduct my dynamic model? (I do realise that Martin
Weiss made some suggestions to me for this- thank you)
Thanks for your advice in advance.
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