On Wed, 26 Oct 2011, buehlerman wrote:
Thank you, things seem to be clearer :-)
Great.
Hansen extended this to the linear regression model and proposed to either
compute one test statistic per parameter (which you can do with the "parm"
argument of gefp) or a joint statistic for all parameters. Hansen included
in "all" parameters also the variance,
The "parm" argument of gefp is a nice feature, but what is about the
significance level in test statistic compuation (sctest)? Is there multiple
testing correction applied or should I rather use for this case the double
max statistic as recommended below?
By applying the functional in sctest(), you implicitly correct for the
number of parameters tested. Thus, you don't need to apply another
correction for multiple testing. (The only caveat with the p-values from
sctest() is that these are always asymptotic p-values and may not be exact
in finite samples. And for many functionals these have been determined by
simulation.)
This is discussed in a little bit more detail in
Zeileis A. (2006), Implementing a Class of Structural Change
Tests: An Econometric Computing Approach. _Computational
Statistics & Data Analysis_, *50*, 2987-3008.
doi:10.1016/j.csda.2005.07.001.
The comment quoted below pertains to the fact that Hansen (1992) suggested
to compute one p-value for each individual parameter as well as another
p-value for all parameters jointly. In such a situation, you would have to
apply some multiple testing procedure. The meanL2BB functional in
strucchange only computes the joint p-value.
hth,
Z
An excerpt from page 5 of the paper "A Unified Approach to Structural Change
Tests Based obn F Statistics, OLS Residuals, and ML Scores" (Achim Zeileis):
Hansen (1992) suggests to compute this statistic for the full process efp(t)
to test all coefficients
simultaneously and also for each component of the process (efp(t))j
(denoting the j-th component
of the process efp(t), j = 1, . . . , k) individually to assess which
parameter causes the instability.
*Note, that this approach leads to a violation of the significance level of
the procedure if no multiple
testing correction is applied.* This can be avoided if a functional is
applied to the empirical
fluctuation process which aggregates over time first yielding k independent
test statistics (see
Zeileis and Hornik 2003, for more details).
--
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