| mefp {strucchange} | R Documentation |
Online monitoring of structural breaks in a linear regression model. A parameter estimate based on a historical sample is compared with estimates based on newly arriving data; a sequential test on the difference between the two parameter estimates signals structural breaks.
mefp(obj, ...)
mefp(formula, data, type = c("ME", "fluctuation"), h=1,
alpha=0.05, functional = c("max", "range"), period=10,
tolerance=.Machine$double.eps^0.5,
MECritvalTable=monitorMECritvalTable,
rescale=FALSE, ...)
mefp(obj, alpha=0.05, functional = c("max", "range"),
period=10, tolerance=.Machine$double.eps^0.5,
MECritvalTable=monitorMECritvalTable,
rescale=NULL, ...)
monitor(obj, data=NULL, verbose=TRUE)
formula |
a symbolic describtion for the model to be tested. |
data |
an optional data frame containing the variables in the model. By
default the variables are taken from the environment which efp is
called from. |
type |
specifies which type of fluctuation process will be computed. |
h |
(only used for ME processes). A numeric scalar from interval (0,1) specifying the size of the data window relative to the sample size. |
obj |
Object of class "efp" (for mefp) or
"mefp" (for monitor). |
alpha |
Significance level of the test, i.e., probability of type I error. |
functional |
Determines if maximum or range of parameter differences is used as statistic. |
period |
(only used for ME processes). Maximum time (relative to the history period) that will be monitored. Default is 10 times the history period. |
tolerance |
Tolerance for numeric == comparisons. |
MECritvalTable |
(only used for ME processes). Table of
critical values, this table is interpolated to get critical values
for arbitrary alphas. By default the pre-computed table
monitorMECritvalTable is used. |
rescale |
If TRUE the estimates will be standardized by
the regressor matrix of the corresponding subsample similar to
Kuan & Chen (1994); if FALSE the historic regressor matrix will
be used. In mefp.efp the default is to take the same value as in
the original call of efp. |
verbose |
If TRUE, signal breaks by text output. |
... |
Currently not used. |
mefp creates an object of class "mefp" either
from a model formula or from an object of class "efp". In
addition to the arguments of efp, the type of statistic
and a significance level for the monitoring must be specified. The
monitoring itself is performed by monitor, which can be
called arbitrarily often on objects of class "mefp". If new
data have arrived, then the empirical fluctuation process is computed
for the new data. If the process crosses the boundaries corresponding
to the significance level alpha, a structural break is detected
(and signaled).
The typical usage is to initialize the monitoring by creation of an
object of class "mefp" either using a formula or an
"efp" object. Data available at this stage are considered the
history sample, which is kept fixed during the complete
monitoring process, and may not contain any structural changes.
Subsequent calls to monitor perform a sequential test of the
null hypothesis of no structural change in new data against the
general alternative of changes in one or more of the coefficients of
the regression model.
Friedrich Leisch
Friedrich Leisch, Kurt Hornik, and Chung-Ming Kuan. Monitoring structural changes with the generalized fluctuation test. Econometric Theory, 16:835-854, 2000.
df1 <- data.frame(y=rnorm(300))
df1[150:300,"y"] <- df1[150:300,"y"]+1
## use the first 50 observations as history period
e1 <- efp(y~1, data=df1[1:50,,drop=FALSE], type="ME", h=1)
me1 <- mefp(e1, alpha=0.05)
## the same in one function call
me1 <- mefp(y~1, data=df1[1:50,,drop=FALSE], type="ME", h=1,
alpha=0.05)
## monitor the 50 next observations
me2 <- monitor(me1, data=df1[1:100,,drop=FALSE])
plot(me2)
# and now monitor on all data
me3 <- monitor(me2, data=df1)
plot(me3)
## Load dataset "USIncExp" with income and expenditure in the US
## and choose a suitable subset for the history period
data(USIncExp)
USIncExp3 <- window(USIncExp, start=c(1969,1), end=c(1971,12))
## initialize the monitoring with the formula interface
me.mefp <- mefp(expenditure~income, type="ME", rescale=TRUE,
data=USIncExp3, alpha=0.05)
## monitor the new observations for the year 1972
USIncExp3 <- window(USIncExp, start=c(1969,1), end=c(1972,12))
me.mefp <- monitor(me.mefp)
## monitor the new data for the years 1973-1976
USIncExp3 <- window(USIncExp, start=c(1969,1), end=c(1976,12))
me.mefp <- monitor(me.mefp)
plot(me.mefp, functional = NULL)