Canova and Hansen (CH) test statistic for the null hypothesis of a stable seasonal pattern.
ch.test(x, type = c("dummy", "trigonometric"), lag1 = FALSE, NW.order = NULL,
sid = NULL, xreg = NULL, pvalue = c("RS", "raw"), rs.nobsreg = 13)A list of class "CHtest" with components:
the value of the test statistics.
the p-values for each test statistics.
a character string describing the type of test.
a character string giving the name of the data.
the value of the input argument type.
the fitted regression model.
the value of the input argument NW.order.
logical, auxiliary element for print method. Were external
regressors defined in the argument xreg?
character, the value of the input argument pvalue.
a vector of characters containing a label related to each p-values.
Auxiliary element for print method.
The method print displays the test statistics and p-values;
summary shows the same output and includes the fitted regression model.
a univariate seasonal time series.
a character string specifying the formulation of the test,
"dummy" for seasonal dummies or "trigonometric" for seasonal cycles.
logical, if TRUE a first order lag of the time series x is included
in the regression model. The default is FALSE.
an integer, the lag truncation parameter to be used in the Newey and West covariance matrix.
an optional numeric vector, the target seasonal dummies or cycles to be tested. By default all the individual and joint test statistics are returned.
an optional vector or matrix of external regressors with the same length or number of
rows as the length of the input time series x.
a character specifying the method employed to compute p-values:
"RS", the default, interpolation based on response surface regressions;
"raw", interpolation in the tabulated values provided in the reference paper
for the Von Mises distribution.
an integer indicating the number of points employed in the response surface
regression (only for pvalue = "RS").
The seasons or seasonal cycles to be tested can be chosen through
an indicator variable defined in the argument sid.
By default, all the \(t\)-statistics
related to each individual dummy or cycle and the joint \(F\)-statistic
are returned.
If type = "dummy", the index of the target seasons can be specified in sid.
For example, in a quarterly series:
sid=c(2) returns the test statistic to the stability of the second quarter;
sid=c(1,3) returns the joint test statistic for the first and third quarters;
sid=c(1,2,3,4) returns the joint test statistic for the null of seasonal
stability at all seasons.
If type = "trigonometric", the indicator vector sid must be of length
floor(frequency(x)/2) and will consist of ones and zeros. Each element in
sid is related to each seasonal cycle according to the same order in which
the seasonal frequencies, \(w_j\), are defined: \(w_j=2\pi j/S\), \(j=1,...,Sh\),
where \(S\) is the periodicity and \(Sh\) is floor(frequency(x)/2).
For example, in a monthly series:
sid=c(0,0,0,0,0,1) returns the test statistic to the stability of the cycle with
frequency \(w_6=\pi\);
sid=c(1,0,0,0,0,1) returns the joint test statistic for cycles related
to frequencies \(w_1=\pi/6\) and \(w_6=\pi\);
sid=c(1,1,1,1,1,1) returns the joint test statistic for the stability of
all seasonal cycles.
The following keywords are also admitted:
sid="all", computes all the test statistic related to each individual season
or cycle as well as the joint test statistic for all seasons or cycles;
sid="joint" computes the joint test statistic for all seasons or cycles.
Canova, F. and Hansen, Bruce E. (1995) "Are seasonal patterns constant over time? A test for seasonal stability". Journal of Business & Economic Statistics, 13(3), pp. 237-252. DOI: tools:::Rd_expr_doi("10.1080/07350015.1995.10524598").
Díaz-Emparanza, I. and Moral, M. P. (2013). Seasonal stability tests in gretl. An application to international tourism data. Working paper: Biltoki D.T. 2013.03. URL: https://addi.ehu.es/handle/10810/10577. Gretl code: https://www.ehu.eus/ignacio.diaz-emparanza/packages/Canova_Hansen.gfn (seems unavailable, so not linked)
ch.rs.pvalue seasonal.cycles,
seasonal.dummies, uroot.raw.pvalue.
library(uroot)
# example for the series "hours" with the same options
# employed in Canova and Hansen (1995)
data("ch-data")
hours <- diff(log(ch.data$hours))
res1 <- ch.test(x = hours, type = "dummy", lag1 = TRUE, NW.order = 4)
res1
# the auxiliary regression is stored in the element "fitted.model"
summary(res1$fit)
if (FALSE) {
# this requires tables not included in the current version of the package
# see note in main documentation file, uroot-package
res2 <- ch.test(x = hours, type = "trigonometric", lag1 = TRUE, NW.order = 4)
res2
summary(res2$fit)}
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