supLM: Generators for efpFunctionals along Continuous Variables

Description

Generators for efpFunctional objects suitable for aggregating empirical fluctuation processes to test statistics along continuous variables (i.e., along time in time series applications).

Usage

supLM(from = 0.15, to = NULL) 
maxMOSUM(width = 0.15)

Arguments

from, to

numeric from interval (0, 1) specifying start and end of trimmed sample period. By default, to is 1 - from, i.e., with the default from = 0.15 the first and last 15 percent of observations are trimmed.

width

a numeric from interval (0,1) specifying the bandwidth. Determines the size of the moving data window relative to sample size.

Value

An object of class efpFunctional.

Details

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.

References

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.

Examples

Run this code

# NOT RUN {
## 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)
# }

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