On Wed, 7 Jan 2015, Allin Cottrell wrote:
It should now be safe to pass a series with non-integral values as
the
dependent variable in ordered probit, provided it has been sucessfully
marked as discrete.
Excuse me, I may be missing something, but I fail to see the logic in
this. In an ordered probit model, the support of the dependent variable is
supposed to be a sequence of increasing numbers, which indicate increasing
"degress of intensity" of a certain unobserved variable, whose conditional
mean is what we're trying to estimate. Of course they could be any
sequence, as long as it's increasing, but I would guess that common sense
dictates they should be increasing _integers_, since it's a purely
conventional way of saying labelling different degrees of intensity across
observations. IMHO allowing the dependent variable to be non-integer could
easily lead to failure to spot an incorrect application of ordered probit
(eg, when you're using the wrong variable from your dataset) and gives you
nothing in return.
As I said before, if the dependent variable is truly quantitative (as
opposed as being a conventional coding for an unobserved continuous latent
variable) and for example 12.5 is just a way of saying "somewhere between
12 and 13" or whatever, the right tool for the job is interval regression,
not ordered probit.
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Riccardo (Jack) Lucchetti
Dipartimento di Scienze Economiche e Sociali (DiSES)
Università Politecnica delle Marche
(formerly known as Università di Ancona)
r.lucchetti(a)univpm.it
http://www2.econ.univpm.it/servizi/hpp/lucchetti
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