[R] strucchange Nyblom-Hansen Test?

[Achim Zeileis]

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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