Description Usage Arguments Details Value See Also Examples
Performs tests for structural change in linear regression models.
1 2 3 4 5 
formula 
a formula describing the model to be tested. 
type 
a character string specifying the structural change test that is
to be performed, the default is 
h 
numeric from interval (0,1) specifying the bandwidth. Determines the size of the data window relative to the sample size (for MOSUM and ME tests only). 
alt.boundary 
logical. If set to 
functional 
indicates which functional should be used to aggregate the empirical fluctuation processes to a test statistic. 
from, to 
numeric. If 
point 
parameter of the Chow test for the potential change point.
Interpreted analogous to the 
asymptotic 
logical. If 
data 
an optional data frame containing the variables in the model. By
default the variables are taken from the environment which

... 
further arguments passed to 
sctest.formula
is a convenience interface for performing structural change
tests in linear regression models based on efp
and Fstats
.
It is mainly a wrapper for sctest.efp
and sctest.Fstats
as it fits an empirical fluctuation process
first or computes the F statistics respectively and subsequently performs the
corresponding test. The Chow test and the NyblomHansen test are available explicitly here.
An alternative convenience interface for performing structural change tests in general
parametric models (based on gefp
) is available in sctest.default
.
An object of class "htest"
containing:
statistic 
the test statistic, 
p.value 
the corresponding p value, 
method 
a character string with the method used, 
data.name 
a character string with the data name. 
sctest.efp
, sctest.Fstats
, sctest.default
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  ## Example 7.4 from Greene (1993), "Econometric Analysis"
## Chow test on Longley data
data("longley")
sctest(Employed ~ Year + GNP.deflator + GNP + Armed.Forces, data = longley,
type = "Chow", point = 7)
## which is equivalent to segmenting the regression via
fac < factor(c(rep(1, 7), rep(2, 9)))
fm0 < lm(Employed ~ Year + GNP.deflator + GNP + Armed.Forces, data = longley)
fm1 < lm(Employed ~ fac/(Year + GNP.deflator + GNP + Armed.Forces), data = longley)
anova(fm0, fm1)
## estimates from Table 7.5 in Greene (1993)
summary(fm0)
summary(fm1)

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