strucchange

supF-, aveF- and expF-Test

Description

Performs the supF-, aveF- or expF-test

Usage

## S3 method for class 'Fstats'
sctest(x, type = c("supF", "aveF", "expF"),
    asymptotic = FALSE, ...)

Arguments

x an object of class “Fstats”.

type a character string specifying which test will be performed.

asymptotic logical. Only necessary if x contains just a single F statistic and type is “supF” or “aveF”. If then set to TRUE the asymptotic (chi-square) distribution instead of the exact (F) distribution will be used to compute the p value.

… currently not used.

Details

If x contains just a single F statistic and type is “supF” or “aveF” the Chow test will be performed.

The original GAUSS code for computing the p values of the supF-, aveF- and expF-test was written by Bruce Hansen and is available from https://www.ssc.wisc.edu/~bhansen/. R port by Achim Zeileis.

Value

An object of class “htest” containing:

statistic the test statistic,

p.value the corresponding p value,

method a character string with the method used,

data.name a character string with the data name.

References

Andrews D.W.K. (1993), Tests for parameter instability and structural change with unknown change point, Econometrica, 61, 821-856.

Andrews D.W.K., Ploberger W. (1994), Optimal tests when a nuisance parameter is present only under the alternative, Econometrica, 62, 1383-1414.

Hansen B. (1992), Tests for parameter instability in regressions with I(1) processes, Journal of Business & Economic Statistics, 10, 321-335.

Hansen B. (1997), Approximate asymptotic p values for structural-change tests, Journal of Business & Economic Statistics, 15, 60-67.

Examples

library("strucchange")

## 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 for potential break points between 1941
## (corresponds to from = 0.5) and 1962 (corresponds to to = 0.85)
## compute F statistics
fs <- Fstats(nhtemp ~ 1, from = 0.5, to = 0.85)
## plot the F statistics
plot(fs, alpha = 0.01)

## and the corresponding p values
plot(fs, pval = TRUE, alpha = 0.01)

## perform the aveF test
sctest(fs, type = "aveF")


    aveF test

data:  fs
ave.F = 10.81, p-value = 2.059e-06