| plot.efp {strucchange} | R Documentation |
Plot and lines method for objects of class "efp"
## S3 method for class 'efp':
plot(x, alpha = 0.05, alt.boundary = FALSE, boundary = TRUE,
functional = "max", main = NULL, ylim = NULL,
ylab = "Empirical fluctuation process", ...)
## S3 method for class 'efp':
lines(x, functional = "max", ...)
x |
an object of class "efp". |
alpha |
numeric from interval (0,1) indicating the confidence level for which the boundary of the corresponding test will be computed. |
alt.boundary |
logical. If set to TRUE alternative boundaries
(instead of the standard linear boundaries) will be plotted (for CUSUM
processes only). |
boundary |
logical. If set to FALSE the boundary will be computed
but not plotted. |
functional |
indicates which functional should be applied to the
process before plotting and which boundaries should be used. If set to NULL
a multiple process with boundaries for the "max" functional is plotted.
For more details see below. |
main, ylim, ylab, ... |
high-level plot function
parameters. |
Plots are available for the "max" functional for all process types.
For Brownian bridge type processes the maximum or mean squared Euclidian norm
("maxL2" and "meanL2") can be used for aggregating before plotting.
No plots are available for the "range" functional.
Alternative boundaries that are proportional to the standard deviation of the corresponding limiting process are available for processes with Brownian motion or Brownian bridge limiting processes.
efp returns an object of class "efp" which inherits
from the class "ts" or "mts" respectively. The function
plot has a method to plot the
empirical fluctuation process; with sctest the corresponding test for
structural change can be performed.
Brown R.L., Durbin J., Evans J.M. (1975), Techniques for testing constancy of regression relationships over time, Journal of the Royal Statistal Society, B, 37, 149-163.
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.
Krämer 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., Krämer W. (1992), The CUSUM test with OLS residuals, Econometrica, 60, 271-285.
Zeileis A. (2000), p Values and Alternative Boundaries for CUSUM Tests, Working Paper 78, SFB "Adaptive Information Systems and Modelling in Economics and Management Science", Vienna University of Economics, http://www.wu-wien.ac.at/am/wp00.htm#78.
## Load dataset "nhtemp" with average yearly temperatures in New Haven data(nhtemp) ## plot the data plot(nhtemp) ## test the model null hypothesis that the average temperature remains ## constant over the years ## compute Rec-CUSUM fluctuation process temp.cus <- efp(nhtemp ~ 1) ## plot the process plot(temp.cus, alpha = 0.01) ## and calculate the test statistic sctest(temp.cus) ## compute (recursive estimates) fluctuation process ## with an additional linear trend regressor lin.trend <- 1:60 temp.me <- efp(nhtemp ~ lin.trend, type = "fluctuation") ## plot the bivariate process plot(temp.me, functional = NULL) ## and perform the corresponding test sctest(temp.me)