| sctest.Fstats {strucchange} | R Documentation |
Performs the supF-, aveF- or expF-test
## S3 method for class 'Fstats':
sctest(x, type = c("supF", "aveF", "expF"),
asymptotic = FALSE, ...)
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. |
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 http://www.ssc.wisc.edu/~bhansen/. R port by Achim Zeileis.
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 |
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.
## 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")