This is great sir! However, I will also suggest that in the SVAR package
that the SVECM model should be included directly just as in the JMulTi.
Also, the VAR and the VECM impulse response should be allowed to be set to
one standard error and any other form that the cholesky ordering.
Thanks.
On Fri, Jan 24, 2020, 8:12 PM Sven Schreiber <svetosch(a)gmx.net> wrote:
Am 24.01.2020 um 19:31 schrieb anzervas(a)yahoo.com:
Dear all (especially Sven and Riccardo),
A recent strand in SVAR literature uses heteroskedasticity to identify the structural
shocks - those interested in the topic may read mainly Rigobon (Review of Economics and
Statistics 2003), Lanne and Lutkepohl (Journal of Money Credit and Banking 2008) and / or
Bacchiocchi and Fanelli (Oxford Bulletin of Economics and Statistics 2015). If one has 2
variance - covariance matrices, there is no need to add zero or other restrictions to
identify the structural matrices A or B.
Prompted by your request I have just discovered (two minutes ago) the R
package "svars" which is described as doing "Data-Driven Identification
of SVAR Models" and looks very interesting. They explicitly mention Rigobon
2003. Given that one of the authors is Helmut Herwartz my prior
expectation is that the package should be well suited for your problem.
Any suggestions / corrections are welcome. This also seems be a good functionality to add
to the SVAR package (in addition to add AB model functionality to VECMs, for completeness
purposes).
I'm actually quite happy that somebody else already implemented it for
open-source software ;-) Isn't division of labor great?
At the Berlin gretl conference Helmut Lütkepohl (no, there are not that
many Helmuts in Germany, just a mild coincidence) of course already
suggested in his keynote to add such things to gretl. And of course it
would be nice to have. But first of all I'm a bit skeptical at least in the
macro context: We are having so many problems to get the first moments of
the data right, and now we want to base everything on changes of the second
moments? Or as somebody said: With T=100 it just takes one or two
observations to move from a Gaussian to a fat-tailed and/or heteroskedastic
distribution. So the whole thing sounds very tempting (less assumptions
needed), but I would always worry about robustness of those animals in
practice.
Secondly there are always tradeoffs in terms of time and effort. Right now
Jack and I are working on something else in SVAR. (Not sure if I'm already
allowed to say what.)
About AB & SVECM: I'm open to hear demands and arguments, but I don't
think we'll do it just "for completeness". The fundamental question is
always: What is the use case, where is the need?
cheers
sven
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