Plot F Statistics

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Description

Plotting method for objects of class "Fstats"

Usage

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## 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.

See Also

Fstats, boundary.Fstats, sctest.Fstats

Examples

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## 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")

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