set_tradingdays: Set Calendar effects correction in Pre-Processing...
In palatej/rjd3toolkit: Utility Functions around 'JDemetra+ 3.0'

set_tradingdays

R Documentation

Set Calendar effects correction in Pre-Processing Specification

Description

Function allowing to select the trading-days regressors to be used for calendar correction in the pre-processing step of a seasonal adjustment procedure. The default is "TradingDays", with easter specific effect enabled. (see set_easter)

All the built-in regressors are meant to correct for type of day effect but don't take into account any holiday. To do so user-defined regressors have to be built.

Usage

set_tradingdays(
  x,
  option = c(NA, "TradingDays", "WorkingDays", "TD3", "TD3c", "TD4", "None",
    "UserDefined"),
  calendar.name = NA,
  uservariable = NA,
  stocktd = NA,
  test = c(NA, "None", "Remove", "Add", "Separate_T", "Joint_F"),
  coef = NA,
  coef.type = c(NA, "Fixed", "Estimated"),
  automatic = c(NA, "Unused", "FTest", "WaldTest", "Aic", "Bic"),
  pftd = NA,
  autoadjust = NA,
  leapyear = c(NA, "LeapYear", "LengthOfPeriod", "None"),
  leapyear.coef = NA,
  leapyear.coef.type = c(NA, "Fixed", "Estimated")
)

Arguments

`x`	the specification to customize, must be a "SPEC" class object (see details).
`option`	to specify the set of trading days regression variables: `"TradingDays"` = six contrast variables, each type of day (from Monday to Saturday) vs Sundays; `"WorkingDays"` = one working (week days)/non-working (week-ends) day contrast variable; `"TD3"` = two contrast variables: week-days vs Sundays and Saturdays vs Sundays; `"TD3c"` = two contrast variables: week-days (Mondays to Thursdays) vs Sundays and Fridays+Saturdays vs Sundays; `"TD4"` = three contrast variables: week-days (Mondays to Thursdays) vs Sundays, Fridays vs Sundays, Saturdays vs Sundays; `"None"` = no correction for trading days; `"UserDefined"` = userdefined trading days regressors.
`calendar.name`	name (string) of the user-defined calendar to be taken into account when generating built-in regressors set in 'option' (if not 'UserDefined).(see examples)
`uservariable`	a vector of characters to specify the name of user-defined calendar regressors. When specified, automatically set `option = "UserDefined"`. Names have to be the same as in `modelling_context`, see example.
`stocktd`	a numeric indicating the day of the month when inventories and other stock are reported (to denote the last day of the month, set the variable to 31). When specified, automatically set `option = "None"`. See `stock_td` function for details.
`test`	defines the pre-tests for the significance of the trading day regression variables based on the AICC statistics: `"None"` = the trading day variables are not pre-tested and are included in the model; (REGARIMA/X-13 specific) `"Add"` = the trading day variables are not included in the initial regression model but can be added to the RegARIMA model after the test; `"Remove"` = the trading day variables belong to the initial regression model but can be removed from the RegARIMA model after the test; (TRAMO specific) `"Separate_T"` = a t-test is applied to each trading day variable separately and the trading day variables are included in the RegArima model if at least one t-statistic is greater than 2.6 or if two t-statistics are greater than 2.0 (in absolute terms); `"Joint_F"` = a joint F-test of significance of all the trading day variables. The trading day effect is significant if the F statistic is greater than 0.95.
`coef`	vector of coefficients for the trading-days regressors.
`coef.type`, `leapyear.coef.type`	vector defining if the coefficients are fixed or estimated.
`automatic`	defines whether the calendar effects should be added to the model manually (`"Unused"`) or automatically. During the automatic selection, the choice of the number of calendar variables can be based on the F-Test (`"FTest"`, TRAMO specific), the Wald Test (`"WaldTest"`), or by minimizing AIC or BIC; the model with higher F-value is chosen, provided that it is higher than `pftd`).
`pftd`	(TRAMO SPECIFIC) `numeric`. The p-value used to assess the significance of the pre-tested calendar effects.
`autoadjust`	a logical indicating if the program corrects automatically the raw series for the leap year effect if the leap year regressor is significant. Only used when the data is log transformed.
`leapyear`	a `character` to specify whether or not to include the leap-year effect in the model: `"LeapYear"` = leap year effect; `"LengthOfPeriod"` = length of period (REGARIMA/X-13 specific), `"None"` = no effect included. Default: a leap year effect regressor is included with any built-in set of trading day regressors.
`leapyear.coef`	coefficient of the leap year regressor.

Details

x specification parameter must be a JD3_X13_SPEC" class object generated with rjd3x13::x13_spec() (or "JD3_REGARIMA_SPEC" generated with rjd3x13::spec_regarima() or "JD3_TRAMOSEATS_SPEC" generated with rjd3tramoseats::spec_tramoseats() or "JD3_TRAMO_SPEC" generated with rjd3tramoseats::spec_tramo()).

References

More information on calendar correction in JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/a-calendar-correction

Examples

# Pre-defined regressors
# y_raw<-ABS$X0.2.09.10.M
# init_spec <- rjd3x13::x13_spec("RSA5c")
# new_spec<-set_tradingdays(init_spec,
#                          option = "TD4",
#                          test =  "None",
#                        coef=c(0.7,NA,0.5),
#        coef.type=c("Fixed","Estimated","Fixed"),
#        leapyear="LengthOfPeriod",
#        leapyear.coef=0.6)
# sa<-rjd3x13::x13(y_raw,new_spec)

# Pre-defined regressors based on user-defined calendar
### create a calendar
BE <- national_calendar(list(
    fixed_day(7, 21),
    special_day("NEWYEAR"),
    special_day("CHRISTMAS"),
    special_day("MAYDAY"),
    special_day("EASTERMONDAY"),
    special_day("ASCENSION"),
    special_day("WHITMONDAY"),
    special_day("ASSUMPTION"),
    special_day("ALLSAINTSDAY"),
    special_day("ARMISTICE")
))
## put into a context
my_context <- modelling_context(calendars = list(cal = BE))
## create a specification
# init_spec <- rjd3x13::x13_spec("RSA5c")
## modify the specification
# new_spec<-set_tradingdays(init_spec,
#                          option = "TradingDays", calendar.name="cal")
## estimate with context
# sa<-rjd3x13::x13(y_raw,new_spec, context=my_context)

# User-defined regressors
# init_spec <- rjd3x13::x13_spec("RSA5c")
# add regressors to context
# variables<-list(Monday,Tuesday, Wednesday,
# Thursday, Friday, Saturday)
# my_context<-modelling_context(variables=variables)
# create a new spec (here default group name: r)
# new_spec<-set_tradingdays(init_spec,
#                          option = "UserDefined",
# uservariable=c("r.Monday","r.Tuesday","r.Wednesday","r.Thursday","r.Friday","r.Saturday"),
# test = "None")
# estimate with context
# sa<-rjd3x13::x13(y_raw,new_spec, context=my_context)

palatej/rjd3toolkit documentation built on Oct. 30, 2024, 10:46 p.m.