Generators for efpFunctionals along Continuous Variables
Generators for efpFunctional objects suitable for aggregating
empirical fluctuation processes to test statistics along continuous
variables (i.e., along time in time series applications).
supLM(from = 0.15, to = NULL) maxMOSUM(width = 0.15)
from, to |
numeric from interval (0, 1) specifying start and end
of trimmed sample period. By default, |
width |
a numeric from interval (0,1) specifying the bandwidth. Determines the size of the moving data window relative to sample size. |
supLM and maxMOSUM generate efpFunctional
objects for Andrews' supLM test and a (maximum) MOSUM test, respectively,
with the specified optional parameters (from and to,
and width, respectively). The resulting objects can be used in
combination with empirical fluctuation processes of class gefp
for significance testing and visualization. The corresponding statistics
are useful for carrying out structural change tests along a continuous
variable (i.e., along time in time series applications). Further typical
efpFunctionals for this setting are the double-maximum
functional maxBB and the Cramer-von Mises functional
meanL2BB.
An object of class efpFunctional.
Merkle E.C., Zeileis A. (2013), Tests of Measurement Invariance without Subgroups: A Generalization of Classical Methods. Psychometrika, 78(1), 59–82. doi:10.1007/S11336-012-9302-4
Zeileis A. (2005), A Unified Approach to Structural Change Tests Based on ML Scores, F Statistics, and OLS Residuals. Econometric Reviews, 24, 445–466. doi:10.1080/07474930500406053.
Zeileis A. (2006), Implementing a Class of Structural Change Tests: An Econometric Computing Approach. Computational Statistics & Data Analysis, 50, 2987–3008. doi:10.1016/j.csda.2005.07.001.
Zeileis A., Hornik K. (2007), Generalized M-Fluctuation Tests for Parameter Instability, Statistica Neerlandica, 61, 488–508. doi:10.1111/j.1467-9574.2007.00371.x.
## seatbelt data
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))
## empirical fluctuation process
scus.seat <- gefp(y ~ ylag1 + ylag12, data = seatbelt)
## supLM test
plot(scus.seat, functional = supLM(0.1))
## MOSUM test
plot(scus.seat, functional = maxMOSUM(0.25))
## double maximum test
plot(scus.seat)
## range test
plot(scus.seat, functional = rangeBB)
## Cramer-von Mises statistic (Nyblom-Hansen test)
plot(scus.seat, functional = meanL2BB)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.