seasonal (version 0.40.0)

genhol: Generate Holiday Regression Variables

Description

A replacement for the genhol software by the U.S. Census Bureau, a utility that uses the same procedure as X-12-ARIMA to create regressors for the U. S. holidays of Easter, Labor Day, and Thanksgiving.

Usage

genhol(x, start = 0, end = 0, frequency = 12, center = "none")

Arguments

x

a vector of class "Date", containing the occurences of the holiday. It can be generated with as.Date.

start

integer, shifts the start point of the holiday. Use negative values if start is before the specified date.

end

integer, shifts end point of the holiday. Use negative values if end is before the specified date.

frequency

integer, frequency of the resulting series

center

character string. Either "calendar", "mean" or "none" (default). Centering avoids a bias in the resultign series. Use "calendar" for Easter or Chinese New Year, "mean" for Ramadan.

Value

• an object of class "ts" that can be used as a user defined variable in seas.

Details

The resulting time series can be used as a user defined variable in seas. Usually, you want the holiday effect to be removed from the final series, so you need to specify regression.usertype = "holiday". (The default is to include user defined variables in the final series.)

References

United States Census Bureau, Notes on centering holiday regressors: http://www.census.gov/srd/www/genhol/genhol_center.html

See Also

seas for the main function of seasonal.

Examples

data(holiday)  # dates of Chinese New Year and Easter

### use of genhol

# 10 day before Easter day to one day after, quarterly data:
genhol(easter, start = -10, end = 1, frequency = 4)
genhol(easter, frequency = 2)  # easter is allways in the first half-year

# centering for overall mean or monthly calendar means
genhol(easter, center = "mean")
genhol(easter, center = "calendar")

### replicating X-13's built-in Easter adjustment

# built-in
m1 <- seas(x = AirPassengers,
regression.variables = c("td1coef", "easter[1]", "ao1951.May"),
arima.model = "(0 1 1)(0 1 1)", regression.aictest = NULL,
outlier = NULL, transform.function = "log", x11 = list())
summary(m1)

# user defined variable
ea1 <- genhol(easter, start = -1, end = -1, center = "calendar")

# regression.usertype = "holiday" ensures that the effect is removed from
# the final series.
m2 <- seas(x = AirPassengers,
           regression.variables = c("td1coef", "ao1951.May"),
           xreg = ea1, regression.usertype = "holiday",
           arima.model = "(0 1 1)(0 1 1)", regression.aictest = NULL,
           outlier = NULL, transform.function = "log", x11 = list())
summary(m2)

all.equal(final(m2), final(m1), tolerance = 1e-06)


# with genhol, its possible to do sligtly better, by adjusting the length
# of easter

ea2 <- genhol(easter, start = -2, end = +1, center = "calendar")
m3 <- seas(x = AirPassengers,
           regression.variables = c("td1coef", "ao1951.May"),
           xreg = ea2, regression.usertype = "holiday",
           arima.model = "(0 1 1)(0 1 1)", regression.aictest = NULL,
           outlier = NULL, transform.function = "log", x11 = list())
summary(m3)


### Chinese New Year

data(cntrade)  # exports and imports of China
data(holiday)  # dates of Chinese New Year and Easter

# de facto holiday length: http://en.wikipedia.org/wiki/Chinese_New_Year
cny.ts <- genhol(cny, start = 0, end = 6, center = "calendar")

m1 <- seas(x = imp, xreg = cny.ts, regression.usertype = "holiday", x11 = list(),
           regression.variables = c("td1coef", "ls1985.Jan", "ls2008.Nov"),
           arima.model = "(0 1 2)(0 1 1)", regression.aictest = NULL,
           outlier = NULL, transform.function = "log")
summary(m1)

# compare to identical no-CNY model:
m2 <- seas(x = imp, x11 = list(),
           regression.variables = c("td1coef", "ls1985.Jan", "ls2008.Nov"),
           arima.model = "(0 1 2)(0 1 1)", regression.aictest = NULL,
           outlier = NULL, transform.function = "log")
summary(m2)

ts.plot(final(m1), final(m2), col = c("red", "black"))