[Gretl-users] Graph two densities together and Constant in Log Likelihood

Alecos Papadopoulos papadopalex at aueb.gr
Tue Jul 9 22:00:32 EDT 2013

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 

Alecos Papadopoulos
Athens University of Economics and Business, Greece
Department of Economics
fax: +30-210-8259763

>> 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
>> 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.
> -------------------------------------------------------
>     Riccardo (Jack) Lucchetti
>     Dipartimento di Scienze Economiche e Sociali (DiSES)
>     Universit? Politecnica delle Marche
>     (formerly known as Universit? di Ancona)
>     r.lucchetti at univpm.it
>     http://www2.econ.univpm.it/servizi/hpp/lucchetti
> -------------------------------------------------------
> ------------------------------

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