Re: [Gretl-users] Graph two densities together and Constant in Log Likelihood
Ricardo (Jack), thanks for the continuing support.
GRAPH TWO DENSITIES TOGETHER : I saw the function you directed me to in
action using the examples with the series from the gretl data bases, now
I am trying to understand what it actually does and whether it suits my
purposes... Exploration is fun.
CONSTANT IN THE LOG-LIKELIHOOD: I will try what you suggest, and also I
few more things I have in mind, and will report back. Just a note that
in model 2 (that is virtually identical to model 1 as regards to
estimates and final gradient values), the value of the logl appears
positive.
Alecos Papadopoulos
Athens University of Economics and Business, Greece
Department of Economics
cell:+30-6945-378680
fax: +30-210-8259763
skype:alecos.papadopoulos
> GRAPH OF TWO DENSITIES TOGETHER: Thanks for providing the older
link.
> Although the code there is to plot two densities /consecutively /from left to
> right, while what I need to do is to /superimpose/ them - and this I realize
> now has the problem of having two different abscissaes series. Still, I
> learned something new about handling plots in Gretl.
Really? Have you seen this?
http://lists.wfu.edu/pipermail/gretl-users/2013-April/008747.html
> CONSTANT IN LOG-LIKELIHOOD
> The basic code *without the constant in the log-l *is (omitting the initial
> part where OLS executes to obtain initial values)
[...]
> *COMMENT: **slope coefficients are again comparable and the value of the
> likelihood is close to what it should have been if its constant term was
> added afterwards. But the estimates of the three variance terms v0 v1 v2 are
> totally different, the one reaching the specified boundary of the parameter
> space (zero). *
This is very strange indeed. It *may* have something to do with the
machine epsilon of your computer, but still it's very strange. Basically,
models 1 and 2 converge to the same maximum (with negligible differences);
model 3 really doesn't converge at all: BFGS gives you a spurious
convergence message, but you're not on the maximum. Weird.
Here's a couple of things you may try just to see what happens:
* try using "set bfgs_richardson on"; this uses a different algorithm for
computing numerical derivatives. Slower, but much more accurate.
* re-parametrise your model so to avoid estimating quantities, such as
variances, which have a lower bound. Try logarithms instead, for example.
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Riccardo (Jack) Lucchetti
Dipartimento di Scienze Economiche e Sociali (DiSES)
Universit? Politecnica delle Marche
(formerly known as Universit? di Ancona)
r.lucchetti(a)univpm.it
http://www2.econ.univpm.it/servizi/hpp/lucchetti
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