Dear Riccardo,
I noticed the Pseudo-point observation just after sending the
question. This is a nice way how to see the differences of the gretl
output:
Is it possible to detect that observation (pseudo-point)? I excluded
the observation with the shortest range (just for testing purposes).
The difference of hi and low interval bounds are 2.257e-6 for that
observation. However gretl still detects one pseudo-point observation
after excluding this observation. So I am not sure if I have excluded
the right observation. What are the conditions for an interval to be
transformed as a pseudo-point?
Thank you for your statistical comments on the model. I will study them.
Martins
On 16 June 2014 12:35, Riccardo (Jack) Lucchetti <r.lucchetti(a)univpm.it> wrote:
On Mon, 16 Jun 2014, Mārtiņš Liberts wrote:
> Could you please explain briefly what has been changed for intreg at
> the gretl version 1.9.13?
In brief: we introduced the concept of a "pseudo-point" observation, to deal
with cases when the interval between the high and low bounds is so narrow,
and so away from 0, that the relevant loglikelihood cannot be computed
reliably. In that case, the observation is treated as if it was a point
observation, with value equal to the midpoint. According to your output
files, this happens for 1 observation out of 6170, but is enough to alter
substantially the shape of the log-likelihood, so the algorithm converges to
a slightly different maximum.
It's difficult to be more specific without looking at the data, but I can't
help noticing that the normality test strongly rejects the null in both
output files; therefore, I'm inclined to think that the normal-based
interval model is not particularly appropriate for your data, and the issue
you're experiencing is just a by-product of an unfortunate choice for the
measurement of the dependent variable. In other terms, the assumption that
you have a latent variable y* which is normally distributed (conditional on
your explanatory variable) is quite difficult to justify. In these cases,
you should either re-define your dependent variable (are logs an option, for
example?), or re-define your model, by using other densities than the normal
(the gamma density, maybe, if your dependent variable is non-negative?). If
you choose the latter option, you'll have to code your estimator via mle, or
use some other non-parametric alternative.
Hope this helps!
-------------------------------------------------------
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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