R: Plot F Statistics

plot.Fstats {strucchange}

R Documentation

Plot F Statistics

Description

Plotting method for objects of class "Fstats"

Usage

## S3 method for class 'Fstats':
plot(x, pval = FALSE, asymptotic = FALSE, alpha = 0.05,
    boundary = TRUE, aveF = FALSE, xlab = "Time", ylab = NULL,
    ylim = NULL, ...)

Arguments

`x`	an object of class `"Fstats"`.
`pval`	logical. If set to `TRUE` the corresponding p values instead of the original F statistics will be plotted.
`asymptotic`	logical. If set to `TRUE` the asymptotic (chi-square) distribution instead of the exact (F) distribution will be used to compute the p values (only if `pval` is `TRUE`).
`alpha`	numeric from interval (0,1) indicating the confidence level for which the boundary of the supF test will be computed.
`boundary`	logical. If set to `FALSE` the boundary will be computed but not plotted.
`aveF`	logical. If set to `TRUE` the boundary of the aveF test will be plotted. As this is a boundary for the mean of the F statistics rather than for the F statistics themselves a dashed line for the mean of the F statistics will also be plotted.
`xlab, ylab, ylim, ...`	high-level `plot` function parameters.

References

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

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

## 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")