efp(formula, data, type = <>, h = 0.15,
dynamic = FALSE, rescale = TRUE) efp is
called from.TRUE the lagged observations are included as
a regressor.TRUE the estimates will be standardized by
the regressor matrix of the corresponding subsample according to Kuan & Chen
(1994); if FALSE the whole regressor matrix will be used.
(only if type efp returns a list of class "efp" with components inlcuding
"ts" or "mts" respectively,}type of the process fitted,h used.type is one of "Rec-CUSUM", "OLS-CUSUM",
"Rec-MOSUM" or "OLS-MOSUM" the function efp will return a
one-dimensional empiricial process of sums of residuals. Either it will be based
on recursive residuals or on OLS residuals and the process will contain
CUmulative SUMs or MOving SUMs of residuals in a certain data window.
For the MOSUM and ME processes all estimations are done for the
observations in a moving data window, whose size is determined by h and
which is shifted over the whole sample.If type is either "RE" or "ME" a
k-dimensional process will be returned, if k is the number of
regressors in the model, as it is based on recursive OLS estimates of the
regression coefficients or moving OLS estimates respectively. The recursive
estimates test is also called fluctuation test, therefore setting type
to "fluctuation" was used to specify it in earlier versions of
strucchange. It still can be used now, but will be forced to "RE".
If type is "Score-CUSUM" or "Score-MOSUM" a k+1-dimensional
process will be returned, one for each score of the regression coefficients and one for
the scores of the variance. The process gives the decorrelated cumulative sums of the ML
scores (in a gaussian model) or first order conditions respectively (in an OLS framework).
If there is a single structural change point $t^*$, the recursive CUSUM path starts to depart from its mean 0 at $t^*$. The Brownian bridge type paths will have their respective peaks around $t^*$. The Brownian bridge increments type paths should have a strong change at $t^*$.
The function plot
has a method to plot the empirical fluctuation process; with
sctest the corresponding test on structural change can be
performed.
Chu C.-S., Hornik K., Kuan C.-M. (1995), MOSUM tests for parameter constancy, Biometrika, 82, 603-617.
Chu C.-S., Hornik K., Kuan C.-M. (1995), The moving-estimates test for parameter stability, Econometric Theory, 11, 669-720.
Hansen B. (1992), Testing for Parameter Instability in Linear Models, Journal of Policy Modeling, 14, 517-533.
Hjort N.L., Koning A. (2002), Tests for Constancy of Model Parameters Over Time, Nonparametric Statistics, 14, 113-132.
Kr�mer W., Ploberger W., Alt R. (1988), Testing for structural change in dynamic models, Econometrica, 56, 1355-1369.
Kuan C.-M., Hornik K. (1995), The generalized fluctuation test: A unifying view, Econometric Reviews, 14, 135 - 161.
Kuan C.-M., Chen (1994), Implementing the fluctuation and moving estimates tests in dynamic econometric models, Economics Letters, 44, 235-239.
Ploberger W., Kr�mer W. (1992), The CUSUM test with OLS residuals, Econometrica, 60, 271-285.
Zeileis A., Leisch F., Hornik K., Kleiber C. (2002), strucchange:
An R Package for Testing for Structural Change in Linear Regression Models,
Journal of Statistical Software, 7(2), 1-38.
Zeileis A., Hornik K. (2003), Generalized M-Fluctuation Tests for Parameter
Instability, Report 80, SFB "Adaptive Information Systems and Modelling in Economics
and Management Science", Vienna University of Economics,
plot.efp, print.efp,
sctest.efp, boundary.efpif(! "package:stats" %in% search()) library(ts)
## Nile data with one breakpoint: the annual flows drop in 1898
## because the first Ashwan dam was built
data(Nile)
plot(Nile)
## test the null hypothesis that the annual flow remains constant
## over the years
## compute OLS-based CUSUM process and plot
## with standard and alternative boundaries
ocus.nile <- efp(Nile ~ 1, type = "OLS-CUSUM")
plot(ocus.nile)
plot(ocus.nile, alpha = 0.01, alt.boundary = TRUE)
## calculate corresponding test statistic
sctest(ocus.nile)
## UK Seatbelt data: a SARIMA(1,0,0)(1,0,0)_12 model
## (fitted by OLS) is used and reveals (at least) two
## breakpoints - one in 1973 associated with the oil crisis and
## one in 1983 due to the introduction of compulsory
## wearing of seatbelts in the UK.
data(UKDriverDeaths)
seatbelt <- log10(UKDriverDeaths)
seatbelt <- cbind(seatbelt, lag(seatbelt, k = -1), lag(seatbelt, k = -12))
colnames(seatbelt) <- c("y", "ylag1", "ylag12")
seatbelt <- window(seatbelt, start = c(1970, 1), end = c(1984,12))
plot(seatbelt[,"y"], ylab = expression(log[10](casualties)))
## use RE process
re.seat <- efp(y ~ ylag1 + ylag12, data = seatbelt, type = "RE")
plot(re.seat)
plot(re.seat, functional = NULL)
sctest(re.seat)Run the code above in your browser using DataLab