Hello again, I was away for a few days.
Back to this issue.
I find this interesting too. Periklis, if you've successfully used libsvm
for econometric purposes, please give us a (at least) a brief
description.
Libsvm looks like something we could work with quite easily.
The profile of machine learning has been raised recently by the article by
Mullainathan and Spiess in the Spring 2017 issue of the Journal of Economic
Perspectives, and it's possible this is something we'd want to explore in
gretl.
Allin Cottrell
In my research I have used Support Vector Machines/Regression in
forecasting both financial and macroeconomic variables (I attach two
relevant papers, one on exchange rate forecasting and one on housing prices
). These applications of ML were coded using matlab.
The thing with the SVM/R is to fine-tune the hyperparameters and the kernel
parameters. Libsvm already provides ready wrappers for matlab and R that
are used to import values and export results (available in their page). The
main optimization routine is written in C so it is quite fast. All you need
to build is the wrapper to link libsvm libraries with gretl. Of course you
could also develop you own functions for cross validation training and
parameter seach. That shouldn't be that hard.
It would be nice to be able to use Gretl for Machine Learning!