[Gretl-users] Re: BayTool Package
Am 16.03.2024 um 17:22 schrieb Brian Revell:
Hi
I have tried using the BayTool package with some success (in terms of
getting output),
I have read (perhaps not well enough) the detailed paper by Luca Pedini.
Luca has
had some email problems lately, perhaps he didn't get this one.
However, I am puzzled why when simply using the linear model with
conjugate prior, as a default I get two sets of posterior mean
parameter and se estimates -NI-post m and I post-m and NI-Pst se and
I-post se. . Looking at the graphs of the Gaussian Posterior density
estimates, they appear to more closely centred on the I-pos -mean.
Although the difference s between the N & O posterior estimates is
small, what is the basis for selecting one rather than the other?
I believe that NI
stands for non-informative and I for informative,
which typically refers to the prior used. So basically the NI results
should be purely data-driven, which usually means the max-likelihood or
frequentist quantities. This is just for comparison, a Bayesian would of
course go for the informative ones. (Provided she really believes in the
prior, which for some reason doesn't seem to happen all that often in
reality, but that's a different story.)
Second question - how can I retrieve the fitted values and residuals
from theB-Took Package. Hopefully it resides somewhere to be
retirieved post estimation. Or is iit necessary to run the input data
through the equation to generate it onself -and if so, which set of
posterior mean estimates does one use?
Luca may correct me eventually, but it seems those things are not
directly available. I guess this makes sense from a Bayesian perspective
since it gives you densities, not point estimates. For example, do you
want to consider the posterior mean, median or mode as your quasi-point
estimate? All imply different sets of residuals. In principle they could
all be stored of course, but it's not clear whether that's the most
practical thing to do.
Having said that, it looks like for the coefficients (in the sub-bundle
post_summ) the means are stored, so in that context this arbitrary
choice was made, apparently.
So for the CASE 1 in the sample script with covariates list X, I guess
you could try:
lincomb(X, out.post_summ.post_betamean)
to get the fitted values. (Untested.)
PS -the BayTool package does not seem to attach permanently to the URL
"model" list of options.
Sorry, I don't understand. What URL?
cheers
sven
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