Structural Change Tests in Linear Regression Models
Performs tests for structural change in linear regression models.
## S3 method for class 'formula' sctest(formula, type = , h = 0.15, alt.boundary = FALSE, functional = c("max", "range", "maxL2", "meanL2"), from = 0.15, to = NULL, point = 0.5, asymptotic = FALSE, data, ...)
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
|
... |
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 Nyblom-Hansen 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. |
## 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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