Description Usage Arguments Details Value References See Also Examples

Generators for `efpFunctional`

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

1 2 3 |

`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
`efpFunctional`

s 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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
## 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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