On Fri, Nov 11, 2022 at 5:28 AM AgustÃn Alonso RodrÃguez
<aalonso(a)rcumariacristina.com> wrote:
How to refine or simplify a VAR model suppressing the insignificant estimated coefs?
You have a reply from Sven, noting that gretl does not currently have
a built-in procedure for what you describe. That's true, but I'll
offer a somewhat different perspective.
When you estimate a VAR with p lags of each variable, you should not
be too concerned with the "statistical significance" of each
coefficient -- and not too quick to eliminate terms that are
"insignificant". If the VAR is in levels, in particular, it's likely
that the several lags of a given variable will be strongly correlated.
This then gives you the classic "multicollinearity" effect, whereby
each individual coefficient may be "insignificant" yet a joint test
strongly rejects the null hypothesis that the "collinear" variables
all have coefficients equal to zero. Gretl shows you the joint (F)
tests after estimating a VAR -- that is, tests for Granger causality
-- and you might be better off using these to eliminate one or more
variables from your specification (if warranted) rather than worrying
about individual lagged terms.
Allin Cottrell