Hans Christian:
Am I right supposing that the "breakpoints()" function in the strucchange
package is an implementation of the pure structural change model proposed
by Bai and Perron (1997, 2003)?
Yes, see ?breakpoints. In the references section there is a pointer to our
CSDA 2003 paper, a preprint version of which can be found on my webpage.
My question relates to a partial structural change model that Bai and
Perron formulate in their 2003 paper, e.g. formulated as
y = x' beta + z' delta_j + epsilon,
where beta and delta are vectors of coefficients. x represents a vector of
variables with an invariant effect on y. z represents a vector of
variables, of which the effect on y is allowed to vary due to structural
breaks. j indicates the respective segments, which result from the
structural breaks.
As far as I understand, the "breakpoints()" function can only be used to
estimate equations where beta has dim (0x0), i.e. all coefficients are
subject to structural breaks (at the same time).
Is it possible to tell R that a certain variable is not subject to
structural breaks? Put differently, is there a way to estimate partial
structural change models using the strucchange package?
Simple answer, no, it's not implemented at the moment. The only way to do
something similar would be to estimate the full structural change model
and use its breakpoints to fit the partial structural change model. In
pseudocode:
## estimate full structural change model
bp <- breakpoints(y ~ x + z)
## fit full structural change model
fm1 <- lm(y ~ breakfactor(bp) / (x + z))
## fit partial structural change model
fm2 <- lm(y ~ x + breakfactor(bp)/z)
The main disadvantage is that the breakpoints are potentially suboptimal
in the partial change model. In many practical applications, they are very
similar, though.
Another package which implements partial changes but for a different class
of breakpoints (broken-line trends instead of "jumps") is the "segmented"
package. There is a paper by Vito Muggeo, the maintainer, about it:
http://www.R-project.org/doc/Rnews/Rnews_2008-1.pdf
It might be worth a look for you.
One final point: If you send questions like this to R-help, be sure to Cc
the package maintainer (as the R posting guide, linked below, tells you).
Hope that helps,
Z
Thank you very much for your help (and for the greatest statistical
software, anyway).
Hans Christian
doctoral student, Freie Universität Berlin
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