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. ------------------------------------------------------- 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 -------------------------------------------------------
