R: Generators for efpFunctionals along Continuous Variables

supLM {strucchange} | R Documentation

## 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.

### 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 `efpFunctional`s for this setting are the double-maximum functional `maxBB` and the Cramer-von Mises functional `meanL2BB`.

### Value

An object of class `efpFunctional`.

### 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.

### See Also

`efpFunctional`, `gefp`

### Examples

    ## 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)

[Package strucchange version 1.5-2 Index]