Type:	Package
Title:	Utility Functions Around 'JDemetra+ 3.0'
Version:	3.6.0
Description:	R Interface to 'JDemetra+ 3.x' (https://github.com/jdemetra) time series analysis software. It provides functions allowing to model time series (create outlier regressors, user-defined calendar regressors, Unobserved Components AutoRegressive Integrated Moving Average (UCARIMA) models...), to test the presence of trading days or seasonal effects and also to set specifications in pre-adjustment and benchmarking when using 'rjd3x13' or 'rjd3tramoseats'.
License:	EUPL version 1.1 | EUPL version 1.2 [expanded from: EUPL]
URL:	https://github.com/rjdverse/rjd3toolkit, https://rjdverse.github.io/rjd3toolkit/
BugReports:	https://github.com/rjdverse/rjd3toolkit/issues
Depends:	R (≥ 4.1)
Imports:	checkmate, graphics, methods, rJava (≥ 1.0-6), rjd3jars, RProtoBuf (≥ 0.4.20), stats, utils
Encoding:	UTF-8
Language:	en-GB
LazyData:	TRUE
RoxygenNote:	7.3.3
SystemRequirements:	Java (>= 17)
Collate:	'utils.R' 'jd2r.R' 'protobuf.R' 'arima.R' 'calendars.R' 'calendarts.R' 'decomposition.R' 'deprecated.R' 'differencing.R' 'display.R' 'distributions.R' 'generics.R' 'jd3rslts.R' 'modellingcontext.R' 'procresults.R' 'regarima_generic.R' 'regarima_rslts.R' 'spec_benchmarking.R' 'spec_regarima.R' 'splines.R' 'tests_regular.R' 'tests_seasonality.R' 'tests_td.R' 'timeseries.R' 'variables.R' 'zzz.R'
NeedsCompilation:	no
Packaged:	2026-01-09 11:54:11 UTC; onyxia
Author:	Jean Palate [aut], Alain Quartier-la-Tente [aut], Tanguy Barthelemy [aut, cre, art], Anna Smyk [aut]
Maintainer:	Tanguy Barthelemy <tanguy.barthelemy@insee.fr>
Repository:	CRAN
Date/Publication:	2026-01-13 18:00:02 UTC

Add user-defined variable to a SA model

Description

Add user-defined variable to a SA model

Usage

.add_ud_var(x, jx, userdefined = NULL, out_class = NULL, result = FALSE)

Arguments

Value

A new model with same class as x

Information on the (log-)likelihood

Description

Function allowing to gather information on likelihood estimation

Usage

.likelihood(
  nobs,
  neffectiveobs = NA,
  nparams = 0,
  ll,
  adjustedll = NA,
  aic,
  aicc,
  bic,
  bicc,
  ssq
)

Arguments

Value

Returns a java object of class JD3_LIKELIHOOD.

Examples

# Values used below are taken from the following estimation
# m <- rjd3x13::x13(rjd3toolkit::ABS$X0.2.09.10.M, "rsa3")
# m$result$preprocessing$estimation$likelihood
ll_estimation <- .likelihood(425, 4, 7, 720.2, -2147.407, 4308.14, 4309.09,
                             4333.96, 433.962, 0.0418)

Java Utility Functions

Description

These functions are used in all JDemetra+ 3.0 packages to easily interact between R and Java objects.

Usage

.r2jd_tsdata(s)

.r2jd_tsdomain(period, startYear, startPeriod, length)

.jd2r_tsdata(s)

.jd2r_mts(s)

.jd2r_lts(s)

.jd2r_matrix(s)

.r2jd_matrix(s)

.jdomain(period, start, end)

.enum_sextract(type, p)

.enum_sof(type, code)

.enum_extract(type, p)

.enum_of(type, code, prefix)

.r2p_parameter(r)

.p2r_parameter(p)

.r2p_parameters(r)

.r2p_lparameters(r)

.p2r_parameters(p)

.p2r_parameters_rslt(p)

.p2r_parameters_rsltx(p)

.p2r_test(p)

.p2r_matrix(p)

.p2r_tsdata(p)

.r2p_tsdata(r)

.p2r_parameters_estimation(p)

.p2r_likelihood(p)

.p2r_date(p)

.r2p_date(s)

.p2r_span(span)

.r2p_span(rspan)

.p2r_arima(p)

.p2r_ucarima(p)

.p2r_spec_sarima(spec)

.r2p_spec_sarima(r)

.p2r_outliers(p)

.r2p_outliers(r)

.p2r_sequences(p)

.r2p_sequences(r)

.p2r_iv(p)

.r2p_iv(r)

.p2r_ivs(p)

.r2p_ivs(r)

.p2r_ramps(p)

.r2p_ramps(r)

.p2r_uservars(p)

.r2p_uservars(r)

.p2r_variables(p)

.p2r_sa_decomposition(p, full = FALSE)

.p2r_sa_diagnostics(p)

.p2r_spec_benchmarking(p)

.r2p_spec_benchmarking(r)

.r2jd_sarima(model)

.jd2r_ucarima(jucm)

.p2jd_calendar(pcalendar)

.r2p_calendar(r)

.proc_numeric(rslt, name)

.proc_vector(rslt, name)

.proc_int(rslt, name)

.proc_bool(rslt, name)

.proc_ts(rslt, name)

.proc_str(rslt, name)

.proc_desc(rslt, name)

.proc_test(rslt, name)

.proc_parameter(rslt, name)

.proc_parameters(rslt, name)

.proc_matrix(rslt, name)

.proc_data(rslt, name)

.proc_dictionary(name)

.proc_dictionary2(jobj)

.proc_likelihood(jrslt, prefix)

.r2p_moniker(r)

.p2r_moniker(p)

.r2p_datasupplier(name, r)

.p2r_metadata(p)

.r2p_metadata(r, type)

.p2r_ts(p)

.r2p_ts(r)

.p2r_tscollection(p)

.r2p_tscollection(r)

.r2jd_ts(s)

.jd2r_ts(js)

.r2jd_tscollection(s)

.jd2r_tscollection(js)

.p2r_datasupplier(p)

.r2p_datasuppliers(r)

.p2r_datasuppliers(p)

.p2jd_variables(p)

.jd2p_variables(jd)

.jd2r_variables(jcals)

.r2jd_variables(r)

.p2r_context(p)

.r2p_context(r)

.p2jd_context(p)

.jd2p_context(jd)

.jd2r_modellingcontext(jcontext)

.r2jd_modellingcontext(r)

.p2r_calendars(p)

.r2p_calendars(r)

.p2jd_calendars(p)

.jd2p_calendars(jd)

.jd2r_calendars(jcals)

.r2jd_calendars(r)

.jd3_object(jobjRef, subclasses = NULL, result = FALSE)

.p2r_regarima_rslts(p)

.r2jd_tmp_ts(s, name)

.r2jd_make_ts(source, id, type = "All")

.r2jd_make_tscollection(source, id, type = "All")

current_java_version

minimal_java_version

get_date_min()

get_date_max()

Arguments

Format

An object of class integer of length 1.

An object of class numeric of length 1.

Create a Moniker

Description

Create a Moniker

Usage

.tsmoniker(source, id)

Arguments

Value

Returns a java object of class JD3_TSMONIKER.

Examples

source <- "Txt"
# id is split due to length restrictions
id1 <- "demetra://tsprovider/Txt/20111201/SERIES?datePattern=dd%2FMM%2Fyyyy&delimiter=SEMICOLON&"
id2 <- "file=C%3A%5CDocuments%5CIPI%5CData%5CIPI_nace4.csv#seriesIndex=0"
id <- paste0(id1, id2)
moniker <- .tsmoniker(source, id)

Retail trade statistics in Australia

Description

Retail trade statistics in Australia

Usage

ABS

Format

An object of class data.frame with 425 rows and 22 columns.

Source

ABS

Examples

data(ABS)

Number of births registered in France from 1968 to 2024

Description

Daily number of births recorded in France (metropolitan + DOM), covering the period from January 1, 1968 to December 31, 2024.

Usage

Births

Format

A data frame with 20,820 rows and 2 variables:

• date: Date of the value (from 1968-01-01 to 2024-12-31)

• births: Number of daily births (1254–2830)

Details

The dataset corresponds to the INSEE series T79jnais. The raw data can be downloaded as a CSV file here: https://www.insee.fr/fr/statistiques/fichier/8582123/T79jnais.csv

Source

INSEE, Statistiques de l'état civil – https://www.insee.fr/fr/statistiques/8582123?sommaire=8582147

Examples

data(Births)
plot(Births$date, Births$births,
     type = "l",
     main = "Daily births in France",
     ylab = "Number of births",
     xlab = "date")

French national electricity consumtion

Description

French national electricity consumtion

Usage

Electricity

Format

A data frame with 210384 rows and 3 variables:

• Date: Date of the event (from January 1, 2012 to December 31, 2023)

• Hours: Timestamp of the event (from 00:00 AM to 11:30 PM)

• Consumtion: number of daily birth (29124–102098)

Source

https://www.rte-france.com/en/data-publications/eco2mix/download-indicators

Examples

data(Electricity)

Belgian exports to European countries

Description

Belgian exports to European countries

Usage

Exports

Format

An object of class list of length 34.

Source

NBB

Examples

data(Exports)

Belgian imports from European countries

Description

Belgian imports from European countries

Usage

Imports

Format

An object of class list of length 34.

Source

NBB

Examples

data(Imports)

US Retail trade statistics

Description

US Retail trade statistics

Usage

Retail

Format

An object of class list of length 62.

Source

US-Census Bureau

Examples

data(Retail)

Manage Outliers/Ramps in Specification

Description

Generic function to add outliers or Ramp regressors (add_outlier() and add_ramp()) to a specification or to remove them (remove_outlier() and remove_ramp()).

Usage

add_outlier(x, type, date, name = sprintf("%s (%s)", type, date), coef = 0)

remove_outlier(x, type = NULL, date = NULL, name = NULL)

add_ramp(x, start, end, name = sprintf("rp.%s - %s", start, end), coef = 0)

remove_ramp(x, start = NULL, end = NULL, name = NULL)

Arguments

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()). If a Seasonal adjustment process is performed, each type of Outlier will be allocated to a pre-defined component after the decomposition: "AO" and "TC" to the irregular, "LS" and Ramps to the trend.

Value

The modified specification (with/without outliers or ramp)

References

More information on outliers and other auxiliary variables in JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/

See Also

add_usrdefvar, intervention_variable

Examples

init_spec <- x13_spec_default

# Adding outlier on year 2012
new_spec <- add_outlier(init_spec, type = "AO", date = "2012-01-01")
# Removing outlier on year 2012
new_spec <- remove_outlier(new_spec, type = "AO", date = "2012-01-01")

# Adding ramp on year 2012
new_spec2 <- add_ramp(init_spec, start = "2012-01-01", end = "2012-12-01")
# Removing ramp on year 2012
new_spec2 <- remove_ramp(new_spec2, start = "2012-01-01", end = "2012-12-01")

Add a User-Defined Variable to Pre-Processing Specification.

Description

Function allowing to add any user-defined regressor to a specification and allocate its effect to a selected component, excepted to the calendar component. To add user-defined calendar regressors, set_tradingdays. Once added to a specification, the external regressor(s) will also have to be added to a modelling context before being used in an estimation process. see modelling_context and example.

Usage

add_usrdefvar(
  x,
  group = "r",
  name,
  label = paste0(group, ".", name),
  lag = 0,
  coef = NULL,
  regeffect = c("Undefined", "Trend", "Seasonal", "Irregular", "Series",
    "SeasonallyAdjusted")
)

Arguments

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()). Components to which the effect of the regressor can be allocated:

• "Undefined" : the effect of the regressor is assigned to an additional component, the variable is used to improve the pre-processing step, but is not removed from the series for the decomposition.

  • "Trend": after the decomposition the effect is allocated to the trend component, like a Level-Shift

  • "Irregular": after the decomposition the effect is allocated to the irregular component, like an Additive-outlier

  • "Seasonal": after the decomposition the effect is allocated to the seasonal component, like a Seasonal-outlier

  • "Series": after the decomposition the effect is allocated to the raw series: yc_t=y_t+ effect

  • "SeasonallyAdjusted": after the decomposition the effect is allocated to the seasonally adjusted series: sa_t=T+I+effect

Value

The modified specification (with new user-defined variables)

References

More information on outliers and other auxiliary variables in JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/

See Also

set_tradingdays, intervention_variable

Examples

# Creating one or several external regressors (TS objects),
# which will be gathered in one or several groups
iv1 <- intervention_variable(
    frequency = 12,
    start = c(2000, 1),
    length = 60,
    starts = "2001-01-01",
    ends = "2001-12-01"
)
iv2 <- intervention_variable(
    frequency = 12,
    start = c(2000, 1),
    length = 60,
    starts = "2001-01-01",
    ends = "2001-12-01",
    delta = 1
)

# Using one variable in a a seasonal adjustment process
# Regressors as a list of two groups reg1 and reg2
vars <- list(
    reg1 = list(x = iv1),
    reg2 = list(x = iv2)
)

# Creating the modelling context
my_context <- modelling_context(variables = vars)

# Customize a default specification
init_spec <- x13_spec_default

# Regressors have to be added one by one
new_spec <- add_usrdefvar(init_spec, name = "reg1.iv1", regeffect = "Trend")
new_spec <- add_usrdefvar(new_spec, name = "reg2.iv2", regeffect = "Trend", coef = 0.7)

Aggregation of time series

Description

Makes a frequency change of this series.

Usage

aggregate(
  s,
  nfreq = 1,
  conversion = c("Sum", "Average", "First", "Last", "Min", "Max"),
  complete = TRUE
)

Arguments

Value

A new time series of frequency nfreq.

Examples

s <- ABS$X0.2.09.10.M
# Annual sum
aggregate(s, nfreq = 1, conversion = "Sum") # first and last years removed
aggregate(s, nfreq = 1, conversion = "Sum", complete = FALSE)
# Quarterly mean
aggregate(s, nfreq = 4, conversion = "Average")

Remove an arima model from an existing one.

Description

More exactly, m_diff = m_left - m_right iff m_left = m_right + m_diff.

Usage

arima_difference(left, right, simplify = TRUE)

Arguments

Value

a "JD3_ARIMA" model.

Examples

mod1 <- arima_model(delta = c(1, -2, 1))
mod2 <- arima_model(variance = .01)
diff <- arima_difference(mod1, mod2)
sum <- arima_sum(diff, mod2)
# sum should be equal to mod1

ARIMA Model

Description

ARIMA Model

Usage

arima_model(name = "arima", ar = 1, delta = 1, ma = 1, variance = 1)

Arguments

Value

a "JD3_ARIMA" model.

Examples

model <- arima_model("trend", ar = c(1, -.8), delta = c(1, -1), ma = c(1, -.5), var = 100)

Properties of an ARIMA model

Description

The (pseudo-)spectrum and the auto-covariances of the model are returned

Usage

arima_properties(model, nspectrum = 601, nac = 36)

Arguments

Value

A list with the auto-covariances and with the (pseudo-)spectrum

Examples

mod1 <- arima_model(ar = c(0.1, 0.2), delta = c(1, -1), ma = 0)
arima_properties(mod1)

Sum ARIMA Models

Description

Sum ARIMA Models

Usage

arima_sum(...)

Arguments

Details

Adds several Arima models, considering that their innovations are independent. The sum of two Arima models is computed as follows: the auto-regressive parts (stationary and non stationary of the aggregated model are the smaller common multiple of the corresponding polynomials of the components. The sum of the acf of the modified moving average polynomials is then computed and factorized, to get the moving average polynomial and innovation variance of the sum.

Value

a "JD3_ARIMA" model.

Examples

mod1 <- arima_model(ar = c(0.1, 0.2), delta = 0, ma = 0)
mod2 <- arima_model(ar = 0, delta = 0, ma = c(0.4))
arima_sum(mod1, mod2)

Autocorrelation Functions

Description

Autocorrelation Functions

Usage

autocorrelations(data, mean = TRUE, n = 15)

autocorrelations_partial(data, mean = TRUE, n = 15)

autocorrelations_inverse(data, nar = 30, n = 15)

Arguments

Value

autocorrelations() returns a vector of length n with the autocorrelations. autocorrelations_partial() returns a vector of length n with the partial autocorrelations. autocorrelations_inverse() returns a vector of length n with the inverse autocorrelations.

Examples

x <- ABS$X0.2.09.10.M
autocorrelations(x)
autocorrelations_partial(x)
autocorrelations_inverse(x)

B-Splines

Description

B-Splines

Usage

bsplines(order = 4, knots, pos)

Arguments

Value

A matrix (len(pos) x len(knots))

Examples

s<-bsplines(knots = c(0,.2,.3, .9,.95, 1), pos=seq(0,1,0.01))
matplot(s, type='l')

Trading day regressors with pre-defined holidays

Description

Allows to generate trading day regressors (as many as defined groups), taking into account 7 or less different types of days, from Monday to Sunday, and specific holidays,which are to defined beforehand in a calendar using the functions national_calendar,weighted_calendar or Chained_calendar.

Usage

calendar_td(
  calendar = national_calendar(),
  frequency,
  start,
  length,
  s,
  groups = c(1, 2, 3, 4, 5, 6, 0),
  holiday = 7,
  contrasts = TRUE
)

Arguments

Details

Aggregated values for monthly or quarterly are the numbers of days belonging to a given group, holidays are all summed together in of those groups. Contrasts are the differences between the number of days in a given group (1 to 6) and the number of days in the reference group (0). Regressors are corrected for long-term mean if contrasts = TRUE.

Value

Time series (object of class c("ts","mts","matrix")) corresponding to each group, starting with the 0-group (contrasts = FALSE) or the 1-group (contrasts = TRUE).

References

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

See Also

national_calendar, td

Examples

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")
))
calendar_td(BE, 12, c(1980, 1), 240,
    holiday = 7, groups = c(1, 1, 1, 2, 2, 3, 0),
    contrasts = FALSE
)

Create a Chained Calendar

Description

Allows to combine two calendars, one before and one after a given date.

Usage

chained_calendar(calendar1, calendar2, break_date)

Arguments

Details

A chained calendar is an useful option when major changes in the composition of the holidays take place. In such a case two calendars describing the situation before and after the change of regime can be defined and bound together, one before the break and one after the break.

Value

returns an object of class c("JD3_CHAINEDCALENDAR","JD3_CALENDARDEFINITION")

References

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

See Also

national_calendar, weighted_calendar

Examples

Belgium <- national_calendar(list(special_day("NEWYEAR"), fixed_day(7, 21)))
France <- national_calendar(list(special_day("NEWYEAR"), fixed_day(7, 14)))
chained_cal <- chained_calendar(France, Belgium, "2000-01-01")

Removal of missing values at the beginning/end

Description

Removal of missing values at the beginning/end

Usage

clean_extremities(s)

Arguments

Value

Cleaned series

Examples

y <- window(ABS$X0.2.09.10.M, start = 1982, end = 2018, extend = TRUE)
y
clean_extremities(y)

Compare the annual totals of two series

Description

Usually a raw series and the corresponding seasonally adjusted series

Usage

compare_annual_totals(raw, sa)

Arguments

Value

The largest annual difference (in percentage of the average level of the seasonally adjusted series)

Examples

s1<- rjd3toolkit::ABS$X0.2.09.10.M
# two raw series for example's sake
s2 <- rjd3toolkit::ABS$X0.2.08.10.M
compare_annual_totals(s1,s2)

Promote a R time series to a "full JDemetra+ time series"

Description

Promote a R time series to a "full JDemetra+ time series"

Usage

data_to_ts(s, name)

Arguments

Value

Returns a java object of class JD3_TS

Examples

s <- ABS$X0.2.09.10.M
t <- data_to_ts(s, "test")

Provides a list of dates corresponding to each period of the given time series

Description

Provides a list of dates corresponding to each period of the given time series

Usage

daysOf(ts, pos = 1)

Arguments

Value

A list of the starting dates of each period

Examples

daysOf(Retail$BookStores)

The Chi-Squared Distribution

Description

Density, (cumulative) distribution function and random generation for chi-squared distribution.

Usage

density_chi2(df, x)

cdf_chi2(df, x)

random_chi2(df, n)

Arguments

Value

The functions density_XXX and cdf_t return numeric vectors of same length as x. The functions random_XXX return random number (numeric vectors) of length n.

Examples

density_chi2(df = 3, 1:10)
cdf_chi2(df = 3, 1:10)
random_chi2(df = 3, n = 10)

The Gamma Distribution

Description

Density, (cumulative) distribution function and random generation for Gamma distribution.

Usage

density_gamma(shape, scale, x)

cdf_gamma(shape, scale, x)

random_gamma(shape, scale, n)

Arguments

Value

The functions density_XXX and cdf_t return numeric vectors of same length as x. The functions random_XXX return random number (numeric vectors) of length n.

Examples

density_gamma(shape = 1, scale = 2, x = 1:10)
cdf_gamma(shape = 1, scale = 2, x = 1:10)
random_gamma(shape = 1, scale = 2, n = 10)

The Inverse-Gamma Distribution

Description

Density, (cumulative) distribution function and random generation for inverse-gamma distribution.

Usage

density_inverse_gamma(shape, scale, x)

cdf_inverse_gamma(shape, scale, x)

random_inverse_gamma(shape, scale, n)

Arguments

Value

The functions density_XXX and cdf_t return numeric vectors of same length as x. The functions random_XXX return random number (numeric vectors) of length n.

Examples

density_inverse_gamma(shape = 1, scale = 2, x = 1:10)
cdf_inverse_gamma(shape = 1, scale = 2, x = 1:10)
random_inverse_gamma(shape = 1, scale = 2, n = 10)

The Inverse-Gaussian Distribution

Description

Density, (cumulative) distribution function and random generation for inverse-gaussian distribution.

Usage

density_inverse_gaussian(shape, scale, x)

cdf_inverse_gaussian(shape, scale, x)

random_inverse_gaussian(shape, scale, n)

Arguments

Value

The functions density_XXX and cdf_t return numeric vectors of same length as x. The functions random_XXX return random number (numeric vectors) of length n.

Examples

density_inverse_gaussian(shape = 1, scale = 2, x = 1:10)
random_inverse_gaussian(shape = 1, scale = 2, n = 10)

The Student Distribution

Description

Probability Density Function (PDF), Cumulative Density Function (CDF) and generation of random variables following a Student distribution.

Usage

density_t(df, x)

cdf_t(df, x)

random_t(df, n)

Arguments

Value

The functions density_XXX and cdf_t return numeric vectors of same length as x. The functions random_XXX return random number (numeric vectors) of length n.

Examples

# Probability density function of T with 2 degrees of freedom.
z <- density_t(df = 2, .01 * seq(-100, 100, 1))
# Generating a random vector with each component drawn from a T(2) distribution
z <- random_t(2, 100)
# Computing the probabilty that the random variable X following a T distribution
# with df degrees of freedom is lower than x
z <- cdf_t(df = 12, x = 1.2)
z
z <- cdf_t(df = 12, x = c(0:10)) # array of values
z

Deprecated functions

Description

Use sa_decomposition() instead of sa.decomposition().

Usage

sa.decomposition(x, ...)

Arguments

Value

"JD3_SADECOMPOSITION" object.

Generic Diagnostics Function

Description

Generic Diagnostics Function

Usage

diagnostics(x, ...)

## S3 method for class 'JD3'
diagnostics(x, ...)

Arguments

Value

"No diagnostic" or a list with the diagnostic part of the model

Examples

decompo <- sadecomposition(
    y = ts(c(112, 118, 132, 129, 121, 135), start = 2000, frequency = 12L),
    sa = ts(c(121.72, 124.52, 125.4, 128.91, 128.84, 126.73), start = 2000, frequency = 12L),
    t = ts(c(122.24, 124.33, 126.21, 127.61, 127.8, 126.94), start = 2000, frequency = 12L),
    s = ts(c(0.92, 0.95, 1.05, 1, 0.94, 1.07), start = 2000, frequency = 12L),
    i = ts(c(1, 1, 0.99, 1.01, 1.01, 1), start = 2000, frequency = 12L),
    mul = TRUE
)
diagnostics(decompo)

Get Dictionary and Result

Description

Extract dictionary of a "JD3_ProcResults" object (dictionary()) and extract a specific value (result()) or a list of values (user_defined()).

Usage

dictionary(object)

result(object, id)

user_defined(object, userdefined = NULL)

Arguments

Value

the function dictionary() returns a character vector with the items that can be extracted from object. The result() function extract an item from the object. The user_defined() function do the same thing as result() but can also extract several element at once and encapsulate the items in a user_defined class object.

Differencing of a series

Description

Differencing of a series

Usage

differences(data, lags = 1, mean = TRUE)

Arguments

Value

The differenced series.

Examples

differences(Retail$BookStores, c(1, 1, 12), FALSE)

The series is differenced till its variance is decreasing.

Description

Automatic differencing

Usage

differencing_fast(data, period, mad = TRUE, centile = 90, k = 1.2)

Arguments

Value

Stationary transformation

• ddata: data after differencing

• mean: mean correction

• differences:

  • lag: ddata(t)=data(t)-data(t-lag)

  • order: order of the differencing

Examples

differencing_fast(log(ABS$X0.2.09.10.M), 12)

Automatic stationary transformation

Description

Automatic processing (identification of the order of the differencing) based on auto-correlations and on mean correction. The series should not be seasonal. Source: Tramo

Usage

do_stationary(data, period)

Arguments

Value

Stationary transformation

• ddata: data after differencing

• mean: mean correction

• differences:

  • lag: ddata(t)=data(t)-data(t-lag)

  • order: order of the differencing

Examples

do_stationary(log(ABS$X0.2.09.10.M), 12)

Display Easter Sunday dates in given period

Description

Allows to display the date of Easter Sunday for each year, in the defined period. Dates are displayed in "YYYY-MM-DD" format and as a number of days since January 1st 1970.

Usage

easter_dates(year0, year1, julian = FALSE)

Arguments

Value

a named numeric vector. Names are the dates in format "YYYY-MM-DD", values are number of days since January 1st 1970.

References

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

See Also

national_calendar, easter_day

Examples

# Dates from 2018(included) to 2023 (included)
easter_dates(2018, 2023)

Set a Holiday on an Easter related day

Description

Allows to define a holiday which date is related to Easter Sunday.

Usage

easter_day(offset, julian = FALSE, weight = 1, validity = NULL)

Arguments

Value

returns an object of class c("JD3_EASTERDAY","JD3_HOLIDAY")

References

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

See Also

national_calendar, fixed_day,special_day,fixed_week_day

Examples

easter_day(1) # Easter Monday
easter_day(-2) # Easter Good Friday
# Corpus Christi 60 days after Easter
# Sunday in Julian calendar with weight 0.5, from January 2000 to December 2020
easter_day(
    offset = 60, julian = TRUE, weight = 0.5,
    validity = list(start = "2000-01-01", end = "2020-12-01")
)

Easter regressor

Description

Allows to generate a regressor taking into account the (Julian) Easter effect in monthly or quarterly time series.

Usage

easter_variable(
  frequency,
  start,
  length,
  s,
  duration = 6,
  endpos = -1,
  correction = c("Simple", "PreComputed", "Theoretical", "None")
)

julianeaster_variable(frequency, start, length, s, duration = 6)

Arguments

Value

A time series (object of class "ts")

References

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

See Also

calendar_td

Examples

# Monthly regressor, five-year long, duration 8 days, effect finishing on Easter Monday
ee <- easter_variable(12, c(2020, 1), length = 5 * 12, duration = 8, endpos = 1)
je <- julianeaster_variable(12, c(2020, 1), length = 5 * 12, duration = 8)

Set a holiday on a Fixed Day

Description

It creates a holiday falling on a fixed day each year, with an optional weight and period of validity, like Christmas which is always celebrated on December 25th.

Usage

fixed_day(month, day, weight = 1, validity = NULL)

Arguments

Value

returns an object of class c("JD3_FIXEDDAY","JD3_HOLIDAY")

References

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

See Also

national_calendar, special_day,easter_day

Examples

day <- fixed_day(7, 21, .9)
day # July 21st, with weight=0.9, on the whole sample
day <- fixed_day(12, 25, .5, validity = list(start = "2010-01-01"))
day # December 25th, with weight=0.5, from January 2010
day <- fixed_day(12, 25, .5, validity = list(start = "1968-02-01", end = "2010-01-01"))
day # December 25th, with weight=0.9, from February 1968 until January 2010

Set a Holiday on a Fixed Week Day

Description

Allows to define a holiday falling on a fixed week day each year, like Labour Day in the USA which is always celebrated on the first Monday of September.

Usage

fixed_week_day(month, week, dayofweek, weight = 1, validity = NULL)

Arguments

Value

returns an object of class c("JD3_FIXEDWEEKDAY","JD3_HOLIDAY")

References

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

See Also

national_calendar, fixed_day,special_day,easter_day

Examples

day <- fixed_week_day(9, 1, 1) # first Monday(1) of September.
day

Daily calendar regressors corresponding to holidays

Description

Allows to generate daily regressors (dummy variables) corresponding to each holiday of a pre-defined calendar.

Usage

holidays(
  calendar,
  start,
  length,
  nonworking = c(6, 7),
  type = c("Skip", "All", "NextWorkingDay", "PreviousWorkingDay"),
  single = FALSE
)

Arguments

Details

The pre-defined in a calendar has to be created with the functions national_calendar or weighted_calendar or weighted_calendar. A many regressors as defined holidays are generated, when the holiday occurs the value is 1 and 0 otherwise. This kind of non-aggregated regressors are used for calendar correction in daily data.

Value

A matrix (class "matrix") where each column is associated to a holiday (in the order of creation of the holiday) and each row to a date.

References

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

See Also

calendar_td

Examples

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")
))
q <- holidays(BE, "2021-01-01", 366 * 10, type = "All")
plot(apply(q, 1, max))

Intervention variable

Description

Function allowing to create external regressors as sequences of zeros and ones. The generated variables will have to be added with add_usrdefvar function will require a modelling context definition with modelling_context to be used in an estimation process.

Usage

intervention_variable(
  frequency,
  start,
  length,
  s,
  starts,
  ends,
  delta = 0,
  seasonaldelta = 0
)

Arguments

Details

Intervention variables are combinations of any possible sequence of ones and zeros (the sequence of ones being defined by the parameters starts and ends) and of \frac{1}{(1-B)^d} and \frac{1}{(1-B^s)^D} where B is the backwards operator, s is the frequency of the time series, d and D are the parameters delta and seasonaldelta.

For example, with delta = 0 and seasonaldelta = 0 we get temporary level shifts defined by the parameters starts and ends. With delta = 1 and seasonaldelta = 0 we get the cumulative sum of temporary level shifts, once differenced the regressor will become a classical level shift.

Value

a ts object

References

More information on auxiliary variables in JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/

See Also

modelling_context, add_usrdefvar

Examples

iv1 <- intervention_variable(
    frequency = 12,
    start = c(2000, 1),
    length = 60,
    starts = "2001-01-01",
    ends = "2001-12-01"
)
plot(iv1)
iv2 <- intervention_variable(
    frequency = 12,
    start = c(2000, 1),
    length = 60,
    starts = "2001-01-01",
    ends = "2001-12-01",
    delta = 1
)
plot(iv2)

# Using one variable in a a seasonal adjustment process
# Regressors as a list of two groups reg1 and reg2
vars <- list(
    reg1 = list(x = iv1),
    reg2 = list(x = iv2)
)

# creating the modelling context
my_context <- modelling_context(variables = vars)

# customize a default specification
init_spec <- x13_spec_default
new_spec <- add_usrdefvar(init_spec, name = "reg1.iv1", regeffect = "Trend")

JD3 print functions

Description

JD3 print functions

Usage

## S3 method for class 'JD3_ARIMA'
print(x, ...)

## S3 method for class 'JD3_UCARIMA'
print(x, ...)

## S3 method for class 'JD3_SARIMA'
print(x, ...)

## S3 method for class 'JD3_SARIMA_ESTIMATION'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'JD3_SPAN'
print(x, ...)

## S3 method for class 'JD3_LIKELIHOOD'
print(x, summary_info = getOption("summary_info"), ...)

## S3 method for class 'JD3_REGARIMA_RSLTS'
print(
  x,
  digits = max(3L, getOption("digits") - 3L),
  summary_info = getOption("summary_info"),
  ...
)

Arguments

Value

The object is returned invisibly.

Ljung-Box Test

Description

Compute Ljung-Box test to check the independence of a data.

Usage

ljungbox(data, k = 1, lag = 1, nhp = 0, sign = 0, mean = TRUE)

Arguments

Value

A c("JD3_TEST", "JD3") object (see statisticaltest() for details).

Examples

ljungbox(random_t(2, 100), lag = 24, k = 1)
ljungbox(ABS$X0.2.09.10.M, lag = 24, k = 1)

Display Long-term means for a set of calendar regressors

Description

Given a pre-defined calendar and set of groups, the function displays the long-term means which would be used to seasonally adjust the corresponding regressors, as the final value using contrasts is "number of days in the group - long term mean".

Usage

long_term_mean(
  calendar,
  frequency,
  groups = c(1, 2, 3, 4, 5, 6, 0),
  holiday = 7
)

Arguments

Details

A long-term mean is a probability based computation of the average value for every period in every group. (see references). For monthly regressors there are 12 types of periods (January to December).

Value

returns an object of class c("matrix","array") with the long term means corresponding to each group/period, starting with the 0-group.

Examples

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")
))
lt <- long_term_mean(BE, 12,
    groups = c(1, 1, 1, 1, 1, 0, 0),
    holiday = 7
)

Leap Year regressor

Description

Allows to generate a regressor correcting for the leap year or length-of-period effect.

Usage

lp_variable(
  frequency,
  start,
  length,
  s,
  type = c("LeapYear", "LengthOfPeriod")
)

Arguments

Value

Time series (object of class "ts")

References

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

See Also

calendar_td

Examples

# Leap years occur in year 2000, 2004, 2008 and 2012
lp_variable(4, start = c(2000, 1), length = 4 * 13)
lper <- lp_variable(12, c(2000, 1), length = 10 * 12, type = "LengthOfPeriod")

Compute a robust median absolute deviation (MAD)

Description

Compute a robust median absolute deviation (MAD)

Usage

mad(data, centile = 50, medianCorrected = TRUE)

Arguments

Value

The median absolute deviation

Examples

y <- rnorm(1000)
m <- rjd3toolkit::mad(y, centile = 70)

Create modelling context

Description

Function allowing to include calendars and external regressors in a format that makes them usable in an estimation process (reg-arima or tramo modelling, stand alone or as pre-processing in seasonal adjustment). The regressors can be created with functions available in the package or come from any other source, provided they are ts class objects.

Usage

modelling_context(calendars = NULL, variables = NULL)

Arguments

Value

list of calendars and variables

References

More information on auxiliary variables in JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/

See Also

add_usrdefvar, intervention_variable

Examples

# Creating one or several external regressors (TS objects), which will
# be gathered in one or several groups
iv1 <- intervention_variable(12, c(2000, 1), 60,
    starts = "2001-01-01", ends = "2001-12-01"
)
iv2 <- intervention_variable(12, c(2000, 1), 60,
    starts = "2001-01-01", ends = "2001-12-01", delta = 1
)

# Regressors as a list of two groups reg1 and reg2
vars <- list(reg1 = list(x = iv1), reg2 = list(x = iv2))

# Creating the modelling context
my_context <- modelling_context(variables = vars)

Monotonic cubic spline

Description

Monotonic cubic spline

Usage

monotonic_cspline(x, y, pos)

Arguments

Value

An array corresponding to the values of the spline at the requested positions

Examples

s<-monotonic_cspline(x = c(0,.2,.3, .9,.95), y= c(1,3,5,8,12), pos=seq(0,1,0.01))
plot(s, type='l')

Create a National Calendar

Description

Will create a calendar as a list of days corresponding to the required holidays. The holidays have to be generated by one of these functions: fixed_day(), fixed_week_day(), easter_day(), special_day() or single_day().

Usage

national_calendar(days = list(), mean_correction = TRUE)

Arguments

Value

returns an object of class c("JD3_CALENDAR","JD3_CALENDARDEFINITION")

References

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

See Also

chained_calendar, weighted_calendar

Examples

# Fictional calendar using all possibilities to set the required holidays
MyCalendar <- national_calendar(list(
    fixed_day(7, 21),
    special_day("NEWYEAR"),
    special_day("CHRISTMAS"),
    fixed_week_day(7, 2, 3), # second Wednesday of July
    special_day("MAYDAY"),
    easter_day(1), # Easter Monday
    easter_day(-2), # Good Friday
    single_day("2001-09-11"), # appearing once
    special_day("ASCENSION"),
    easter_day(
        offset = 60, julian = FALSE, weight = 0.5,
        validity = list(start = "2000-01-01", end = "2020-12-01")
    ), # Corpus Christi
    special_day("WHITMONDAY"),
    special_day("ASSUMPTION"),
    special_day("ALLSAINTSDAY"),
    special_day("ARMISTICE")
))

Natural cubic spline

Description

Natural cubic spline

Usage

natural_cspline(x, y, pos)

Arguments

Value

An array corresponding to the values of the spline at the requested positions

Examples

s<-natural_cspline(x = c(0,.2,.3, .9,.95), y= c(1,3,5,8,12), pos=seq(0,1,0.01))
plot(s, type='l')

Normality Tests

Description

Set of functions to test the normality of a time series.

Usage

bowmanshenton(data)

doornikhansen(data)

jarquebera(data, k = 0, sample = TRUE)

skewness(data)

kurtosis(data)

Arguments

Value

A c("JD3_TEST", "JD3") object (see statisticaltest for details).

Functions

• bowmanshenton(): Bowman-Shenton test

• doornikhansen(): Doornik-Hansen test

• jarquebera(): Jarque-Bera test

• skewness(): Skewness test

• kurtosis(): Kurtosis test

Examples

x <- rnorm(100) # null
bowmanshenton(x)
doornikhansen(x)
jarquebera(x)
skewness(x)
kurtosis(x)

x <- random_t(2, 100) # alternative
bowmanshenton(x)
doornikhansen(x)
jarquebera(x)
skewness(x)
kurtosis(x)

Generating Outlier regressors

Description

Generating Outlier regressors

Usage

ao_variable(frequency, start, length, s, pos, date = NULL)

tc_variable(frequency, start, length, s, pos, date = NULL, rate = 0.7)

ls_variable(frequency, start, length, s, pos, date = NULL, zeroended = TRUE)

so_variable(frequency, start, length, s, pos, date = NULL, zeroended = TRUE)

Arguments

Details

An additive outlier (AO, ao_variable) is defined as:

AO_t = \begin{cases}1 &\text{if } t=t_0 \\ 0 & \text{if }t\ne t_0\end{cases}

A level shift (LS, ls_variable) is defined as (if zeroended = TRUE):

LS_t = \begin{cases}-1 &\text{if } t < t_0 \\ 0 & \text{if }t\geq t_0 \end{cases}

A transitory change (TC, tc_variable) is defined as:

TC_t = \begin{cases} 0 &\text{if }t < t_0 \\ \alpha^{t-t_0} & t\geq t_0 \end{cases}

A seasonal outlier (SO, so_variable) is defined as (if zeroended = TRUE):

SO_t = \begin{cases} 0 &\text{if }t\geq t_0 \\ -1 & \text{if }t < t_0 \text{ and $t$ same periode as }t_0\\ -\frac{1}{s-1} & \text{otherwise }\end{cases}

Value

a ts object

Examples

# Outliers in February 2002
ao <- ao_variable(12, c(2000, 1), length = 12 * 4, date = "2002-02-01")
ls <- ls_variable(12, c(2000, 1), length = 12 * 4, date = "2002-02-01")
tc <- tc_variable(12, c(2000, 1), length = 12 * 4, date = "2002-02-01")
so <- so_variable(12, c(2000, 1), length = 12 * 4, date = "2002-02-01")
plot.ts(ts.union(ao, ls, tc, so),
    plot.type = "single",
    col = c("black", "orange", "green", "gray")
)

Periodic B-Splines

Description

Periodic B-Splines

Usage

periodic_bsplines(order = 4, period = 1, knots, pos)

Arguments

Value

A matrix (len(pos) x len(knots))

Examples

s<-periodic_bsplines(knots = c(0,.2,.3, .9,.95), pos=seq(0,1,0.01))
matplot(s, type='l')

Periodic cubic spline

Description

Periodic cubic spline

Usage

periodic_cspline(x, y, pos)

Arguments

Value

An array corresponding to the values of the spline at the requested positions

Examples

s<-periodic_cspline(x = c(0,.2,.3, .9,.95, 1), y= c(1,3,8,5,12, 1), pos=seq(0,1,0.01))
plot(s, type='l')

Periodic cardinal cubic splines

Description

Periodic cardinal cubic splines

Usage

periodic_csplines(x, pos)

Arguments

Value

A matrix (len(pos) x len(knots))

Examples

s<-periodic_csplines(x = c(0,.2,.3, .9,.95, 1), pos=seq(0,1,0.01))
matplot(s, type='l')

Periodic dummies and contrasts

Description

Periodic dummies and contrasts

Usage

periodic_dummies(frequency, start, length, s)

periodic_contrasts(frequency, start, length, s)

Arguments

Details

The function periodic_dummies() creates as many time series as types of periods in a year (4 or 12) with the value one only for one given type of period (ex Q1) The periodic_contrasts() function is based on periodic_dummies but adds -1 to the period preceding a 1.

Value

a mts object with frequency column

Examples

# periodic dummies for a quarterly series
p <- periodic_dummies(4, c(2000, 1), 60)
# periodic contrasts for a quarterly series
q <- periodic_contrasts(4, c(2000, 1), 60)
q[1:9, ]

Calendars Print Methods

Description

Print functions for calendars

Usage

## S3 method for class 'JD3_FIXEDDAY'
print(x, ...)

## S3 method for class 'JD3_FIXEDWEEKDAY'
print(x, ...)

## S3 method for class 'JD3_EASTERDAY'
print(x, ...)

## S3 method for class 'JD3_SPECIALDAY'
print(x, ...)

## S3 method for class 'JD3_SINGLEDAY'
print(x, ...)

## S3 method for class 'JD3_CALENDAR'
print(x, ...)

## S3 method for class 'JD3_CHAINEDCALENDAR'
print(x, ...)

## S3 method for class 'JD3_WEIGHTEDCALENDAR'
print(x, ...)

Arguments

Value

The object is returned invisibly.

Create Java CalendarTimeSeries

Description

Create Java CalendarTimeSeries

Usage

r2jd_calendarts(calendarobs)

Arguments

Value

a Java object

Examples

# example code
obs <- list(
    list(start = as.Date("1980-01-01"), end = as.Date("1999-12-31"), value = 2000),
    list(start = as.Date("2000-01-01"), end = as.Date("2010-01-01"), value = 1000)
)
jobj <- r2jd_calendarts(obs)

Ramp regressor

Description

Ramp regressor

Usage

ramp_variable(frequency, start, length, s, range)

Arguments

Details

A ramp between two dates t_0 and t_1 is defined as:

RP_t= \begin{cases} -1 & \text{if }t\geq t_0 \\ \frac{t-t_0}{t_1-t_0}-1 & t_0< t < t_1 \\ 0 & t \leq t_1 \end{cases}

Value

a ts object

Examples

# Ramp variable from January 2001 to September 2001
rp <- ramp_variable(12, c(2000, 1), length = 12 * 4, range = c(13, 21))
# Or equivalently
rp <- ramp_variable(12, c(2000, 1), length = 12 * 4, range = c("2001-01-01", "2001-09-02"))
plot.ts(rp)

Range-Mean Regression

Description

Function to perform a range-mean regression, trimmed to avoid outlier distortion. The can be used to select whether the original series will be transformed into log or maintain in level.

Usage

rangemean_tstat(data, period = 0, groupsize = 0, trim = 0)

Arguments

Details

First, the data is divided into n groups of successive observations of length l (groupsize). That is, the first group is formed with the first l observations, the second group is formed with observations 1+l to 2l, etc. Then, for each group i, the observations are sorted and the trim smallest and largest observations are rejected (to avoid outlier distortion). With the other observations, the range (noted y_i) and mean (noted m_i) are computed.

Finally, the following regression is performed :

y_t = \alpha + \beta m_t + u_t.

The function rangemean_tstat returns the T-statistic associated to \beta. If it is significantly higher than 0, log transformation is recommended.

Value

T-Stat of the slope of the range-mean regression.

Examples

y <- ABS$X0.2.09.10.M
# Multiplicative pattern
plot(y)
period <- 12
rm_t <- rangemean_tstat(y, period = period, groupsize = period)
rm_t # higher than 0
# Can be tested:
pt(rm_t, period - 2, lower.tail = FALSE)
# Or :
1 - cdf_t(period - 2, rm_t)

# Close to 0
rm_t_log <- rangemean_tstat(log(y), period = period, groupsize = period)
rm_t_log
pt(rm_t_log, period - 2, lower.tail = FALSE)

Reload dictionaries

Description

Reload dictionaries

Usage

reload_dictionaries()

Value

invisibly NULL

Examples

reload_dictionaries()

Runs Tests around the mean or the median

Description

Functions to compute runs test around the mean or the median (testofruns) or up and down runs test (testofupdownruns) to check randomness of a data.

Usage

testofruns(data, mean = TRUE, number = TRUE)

testofupdownruns(data, number = TRUE)

Arguments

Value

A c("JD3_TEST", "JD3") object (see statisticaltest() for details).

Functions

• testofruns(): Runs test around mean or median

• testofupdownruns(): up and down runs test

Examples

x <- random_t(5, 1000)
# random values
testofruns(x)
testofupdownruns(x)
# non-random values
testofruns(ABS$X0.2.09.10.M)
testofupdownruns(ABS$X0.2.09.10.M)

Generic Preprocessing Function

Description

Generic function for preprocessing defined in other packages.

Usage

sa_preprocessing(x, ...)

Arguments

Value

a list, the preprocessing part of a model.

Generic Function for Seasonal Adjustment Decomposition

Description

Generic function to format the seasonal adjustment decomposition components. sa_decomposition() is a generic function defined in other packages.

Usage

sadecomposition(y, sa, t, s, i, mul)

## S3 method for class 'JD3_SADECOMPOSITION'
print(x, n_last_obs = frequency(x$series), ...)

## S3 method for class 'JD3_SADECOMPOSITION'
plot(
  x,
  first_date = NULL,
  last_date = NULL,
  type_chart = c("sa-trend", "seas-irr"),
  caption = c(`sa-trend` = "Y, Sa, trend", `seas-irr` = "Sea., irr.")[type_chart],
  colors = c(y = "#F0B400", t = "#1E6C0B", sa = "#155692", s = "#1E6C0B", i = "#155692"),
  ...
)

sa_decomposition(x, ...)

Arguments

Value

"JD3_SADECOMPOSITION" object.

Examples

decompo <- sadecomposition(
    y = ts(c(112, 118, 132, 129, 121, 135), start = 2000, frequency = 12L),
    sa = ts(c(121.72, 124.52, 125.4, 128.91, 128.84, 126.73), start = 2000, frequency = 12L),
    t = ts(c(122.24, 124.33, 126.21, 127.61, 127.8, 126.94), start = 2000, frequency = 12L),
    s = ts(c(0.92, 0.95, 1.05, 1, 0.94, 1.07), start = 2000, frequency = 12L),
    i = ts(c(1, 1, 0.99, 1.01, 1.01, 1), start = 2000, frequency = 12L),
    mul = TRUE
)
print(decompo)
plot(decompo)

Decompose SARIMA Model into three components trend, seasonal, irregular

Description

Decompose SARIMA Model into three components trend, seasonal, irregular

Usage

sarima_decompose(model, rmod = 0, epsphi = 0)

Arguments

Value

An UCARIMA model

Examples

model <- sarima_model(period = 12, d = 1, bd = 1, theta = -0.6, btheta = -0.5)
ucm <- sarima_decompose(model)

Estimate SARIMA Model

Description

Estimate SARIMA Model

Usage

sarima_estimate(
  x,
  order = c(0, 0, 0),
  seasonal = list(order = c(0, 0, 0), period = NA),
  mean = FALSE,
  xreg = NULL,
  eps = 1e-09
)

Arguments

Value

An object of class JD3_SARIMA_ESTIMATE containing:

• the estimated parameters,

• the raw data,

• the regressors,

• the standard errors,

• the log-likelihood (with the number of observations, the number of effective observations, the number of parameters, the log-likelihood, the adjusted log-likelihood, the AIC, the AICC, the BIC, the BICC, and the sum of squares),

• the residuals,

• the orders of the model.

Examples

y <- ABS$X0.2.09.10.M
sarima_estimate(y, order = c(0, 1, 1), seasonal = c(0, 1, 1))

Estimate ARIMA Model with Hannan-Rissanen method

Description

Estimate ARIMA Model with Hannan-Rissanen method

Usage

sarima_hannan_rissanen(
  x,
  order = c(0, 0, 0),
  seasonal = list(order = c(0, 0, 0), period = NA),
  initialization = c("Ols", "Levinson", "Burg"),
  biasCorrection = TRUE,
  finalCorrection = TRUE
)

Arguments

Value

An object of class JD3_SARIMA with the estimated coefficient.

Examples

y <- ABS$X0.2.09.10.M
model<- sarima_hannan_rissanen(y, order = c(0, 1, 1), seasonal = c(0, 1, 1))

Seasonal ARIMA model (Box-Jenkins)

Description

Seasonal ARIMA model (Box-Jenkins)

Usage

sarima_model(
  name = "sarima",
  period,
  phi = NULL,
  d = 0,
  theta = NULL,
  bphi = NULL,
  bd = 0,
  btheta = NULL
)

Arguments

Value

A "JD3_SARIMA" model.

Examples

mod1 <- sarima_model(period = 12, d = 1, bd = 1, theta = 0.2, btheta = 0.2)

SARIMA Properties

Description

SARIMA Properties

Usage

sarima_properties(model, nspectrum = 601, nacf = 36)

Arguments

Value

List with the acf and the spectrum of the model.

Examples

mod1 <- sarima_model(period = 12, d = 1, bd = 1, theta = 0.2, btheta = 0.2)
sarima_properties(mod1)

Simulate Seasonal ARIMA

Description

Simulate Seasonal ARIMA

Usage

sarima_random(model, length, stde = 1, tdegree = 0, seed = -1)

Arguments

Value

a numeric vector with the simulated series.

Examples

# Airline model
s_model <- sarima_model(period = 12, d = 1, bd = 1, theta = 0.2, btheta = 0.2)
x <- sarima_random(s_model, length = 64, seed = 0)
plot(x, type = "l")

Canova-Hansen seasonality test

Description

Canova-Hansen seasonality test

Usage

seasonality_canovahansen(
  data,
  period,
  type = c("Contrast", "Dummy", "Trigonometric"),
  lag1 = TRUE,
  kernel = c("Bartlett", "Square", "Welch", "Tukey", "Hamming", "Parzen"),
  order = NA,
  start = 1
)

Arguments

Value

list with the FTest on seasonal variables, the joint test and the details for the stability of the different seasonal variables

Examples

s <- log(ABS$X0.2.20.10.M)
seasonality_canovahansen(s, 12, type = "Contrast")
seasonality_canovahansen(s, 12, type = "Trigonometric")

Canova-Hansen test using trigonometric variables

Description

Canova-Hansen test using trigonometric variables

Usage

seasonality_canovahansen_trigs(
  data,
  periods,
  lag1 = TRUE,
  kernel = c("Bartlett", "Square", "Welch", "Tukey", "Hamming", "Parzen"),
  order = NA,
  original = FALSE
)

Arguments

Value

a numeric vector

Examples

s <- log(ABS$X0.2.20.10.M)
freqs <- seq(0.01, 0.5, 0.001)
sct <- seasonality_canovahansen_trigs(s, 1 / freqs, original = FALSE)
plot(sct, type = "l")

"X12" Test On Seasonality

Description

"X12" Test On Seasonality

Usage

seasonality_combined(
  data,
  period = NA,
  firstperiod = cycle(data)[1],
  mul = TRUE
)

Arguments

Details

Combined test on the presence of identifiable seasonality (see Ladiray and Quenneville, 1999).

Value

a list with several seasonnality tests (kruskalwallis, stable and evolutive)

Examples

s <- do_stationary(log(ABS$X0.2.09.10.M))$ddata
seasonality_combined(s)
seasonality_combined(random_t(2, 1000), 7)

F-test on seasonal dummies

Description

F-test on seasonal dummies

Usage

seasonality_f(data, period = NA, model = c("AR", "D1", "WN"), nyears = 0)

Arguments

Details

Estimation of a model with seasonal dummies. Joint F-test on the coefficients of the dummies.

Value

A c("JD3_TEST", "JD3") object (see statisticaltest() for details).

Examples

seasonality_f(ABS$X0.2.09.10.M, model = "D1")
seasonality_f(random_t(2, 1000), 7)

Friedman Seasonality Test

Description

Friedman Seasonality Test

Usage

seasonality_friedman(data, period = NA, nyears = 0)

Arguments

Details

Non parametric test ("ANOVA"-type).

Value

A c("JD3_TEST", "JD3") object (see statisticaltest() for details).

Examples

s <- do_stationary(log(ABS$X0.2.09.10.M))$ddata
seasonality_friedman(s)
seasonality_friedman(random_t(2, 1000), 12)

Kruskall-Wallis Seasonality Test

Description

Kruskall-Wallis Seasonality Test

Usage

seasonality_kruskalwallis(data, period, nyears = 0)

Arguments

Details

Non parametric test on the ranks.

Value

A c("JD3_TEST", "JD3") object (see statisticaltest() for details).

Examples

s <- do_stationary(log(ABS$X0.2.09.10.M))$ddata
seasonality_kruskalwallis(s)
seasonality_kruskalwallis(random_t(2, 1000), 7)

Modified QS Seasonality Test (Maravall)

Description

Modified QS Seasonality Test (Maravall)

Usage

seasonality_modified_qs(data, period = NA, nyears = 0)

Arguments

Details

Thresholds for p-values: p.9=2.49, p.95=3.83, p.99=7.06, p.999=11.88. Computed on 100.000.000 random series (different lengths). Remark: the length of the series has some impact on the p-values, mainly on short series. Not critical.

Value

The value of the test

Examples

s <- do_stationary(log(ABS$X0.2.09.10.M))$ddata
seasonality_modified_qs(s)

Periodogram Seasonality Test

Description

Periodogram Seasonality Test

Usage

seasonality_periodogram(data, period = NA, nyears = 0)

Arguments

Details

Tests on the sum of a periodogram at seasonal frequencies.

Value

A c("JD3_TEST", "JD3") object (see statisticaltest() for details).

Examples

s <- do_stationary(log(ABS$X0.2.09.10.M))$ddata
seasonality_periodogram(s)
seasonality_periodogram(random_t(2, 1000), 7)

QS (seasonal Ljung-Box) test.

Description

QS (seasonal Ljung-Box) test.

Usage

seasonality_qs(data, period = NA, nyears = 0, type = 1)

Arguments

Value

A c("JD3_TEST", "JD3") object (see statisticaltest() for details).

Examples

s <- do_stationary(log(ABS$X0.2.09.10.M))$ddata
seasonality_qs(s)
seasonality_qs(random_t(2, 1000), 7)

Set ARIMA Model Structure in Pre-Processing Specification

Description

Function allowing to customize the ARIMA model structure when the automatic modelling is disabled.(see example)

Usage

set_arima(
  x,
  mean = NA,
  mean.type = c(NA, "Undefined", "Fixed", "Initial"),
  p = NA,
  d = NA,
  q = NA,
  bp = NA,
  bd = NA,
  bq = NA,
  coef = NA,
  coef.type = c(NA, "Undefined", "Fixed", "Initial")
)

Arguments

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()).

Value

The modified specification (with new ARIMA model)

References

More information on reg-arima modelling in JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/

See Also

set_automodel, set_transform

Examples

# Customize a default specification
init_spec <- x13_spec_default

# Disable automatic arima modelling
new_spec <- set_automodel(init_spec, enabled = FALSE)

# Customize arima model
new_spec <- set_arima(
    x = new_spec,
    mean = 0.2,
    mean.type = "Fixed",
    p = 1,
    d = 2,
    q = 0,
    bp = 1,
    bd = 1,
    bq = 0,
    coef = c(0.6, 0.7),
    coef.type = c("Initial", "Fixed")
)

Set Arima Model Identification in Pre-Processing Specification

Description

Function allowing to customize Arima model identification procedure.

Usage

set_automodel(
  x,
  enabled = NA,
  acceptdefault = NA,
  cancel = NA,
  ub1 = NA,
  ub2 = NA,
  reducecv = NA,
  ljungboxlimit = NA,
  tsig = NA,
  ubfinal = NA,
  checkmu = NA,
  mixed = NA,
  balanced = NA,
  amicompare = NA
)

Arguments

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()).

Value

The modified specification (with new ARIMA parameters)

References

More information on reg-arima modelling in JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/

See Also

set_arima, set_transform

Examples

# Customize a default specification
init_spec <- x13_spec_default
new_spec <- set_automodel(x = init_spec, enabled = FALSE, acceptdefault = TRUE)

Set estimation sub-span and quality check specification

Description

Function allowing to check if the series can be processed and to define a sub-span on which estimation will be performed

Usage

set_basic(
  x,
  type = c(NA, "All", "From", "To", "Between", "Last", "First", "Excluding"),
  d0 = NULL,
  d1 = NULL,
  n0 = 0,
  n1 = 0,
  preliminary.check = NA,
  preprocessing = NA
)

Arguments

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()).

Value

The modified specification with new estimation span

References

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

See Also

set_estimate, set_arima

Examples

# Customize a default specification
init_spec <- x13_spec_default

# Estimation on sub-span between two dates (date d1 is excluded)
new_spec <- set_basic(
    init_spec,
    type = "Between",
    d0 = "2014-01-01",
    d1 = "2019-01-01",
    preliminary.check = TRUE,
    preprocessing = TRUE
)

# Estimation on the first 60 observations
new_spec <- set_basic(
    init_spec,
    type = "First",
    n0 = 60,
    preliminary.check = TRUE,
    preprocessing = TRUE
)

# Estimation on the last 60 observations
new_spec <- set_basic(
    init_spec,
    type = "Last",
    n1 = 60,
    preliminary.check = TRUE,
    preprocessing = TRUE
)

# Estimation excluding 60 observations at the beginning and 36 at the end of the series
new_spec <-set_basic(
    init_spec,
    type = "Excluding",
    n0 = 60,
    n1 = 36,
    preliminary.check = TRUE,
    preprocessing = TRUE
)

Set Benchmarking Specification

Description

Function allowing to perform a benchmarking procedure after the decomposition step in a seasonal adjustment (disabled by default). Here benchmarking refers to a procedure ensuring consistency over the year between seasonally adjusted and raw (or calendar adjusted) data, as seasonal adjustment can cause discrepancies between the annual totals of seasonally adjusted series and the corresponding annual totals of raw (or calendar adjusted) series.

Usage

set_benchmarking(
  x,
  enabled = NA,
  target = c(NA, "CalendarAdjusted", "Original"),
  rho = NA,
  lambda = NA,
  forecast = NA,
  bias = c("None", "Additive", "Multiplicative")
)

Arguments

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()).

Value

The modified specification with new estimation span

References

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

Examples

init_spec <- x13_spec_default
new_spec <- set_benchmarking(
    x = init_spec,
    enabled = TRUE,
    target = "Original",
    rho = 0.8,
    lambda = 0.5,
    forecast = FALSE,
    bias = "None"
)

Set Easter effect correction in Pre-Processing Specification

Description

Set Easter effect correction in Pre-Processing Specification

Usage

set_easter(
  x,
  enabled = NA,
  julian = NA,
  duration = NA,
  test = c(NA, "Add", "Remove", "None"),
  coef = NA,
  coef.type = c(NA, "Estimated", "Fixed"),
  type = c(NA, "Unused", "Standard", "IncludeEaster", "IncludeEasterMonday")
)

Arguments

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()).

Value

The modified specification (with new easter parameters)

References

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

See Also

easter_variable, easter_day

Examples

# Customize a default specification
init_spec <- x13_spec_default
new_spec <- set_easter(
    x = init_spec,
    enabled = TRUE,
    duration = 12,
    test = "None",
    type = "IncludeEasterMonday"
)

Set Numeric Estimation Parameters and Modelling Span

Description

Function allowing to define numeric boundaries for estimation and to define a sub-span on which reg-arima (tramo) modelling will be performed (pre-processing step)

Usage

set_estimate(
  x,
  type = c(NA, "All", "From", "To", "Between", "Last", "First", "Excluding"),
  d0 = NULL,
  d1 = NULL,
  n0 = 0,
  n1 = 0,
  tol = NA,
  exact.ml = NA,
  unit.root.limit = NA
)

Arguments

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()).

Value

The modified specification (with new estimation parameters)

References

More in JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/

See Also

set_basic, set_arima

Examples

# Customize a default specification
init_spec <- tramoseats_spec_default
new_spec <- set_estimate(
    x = init_spec,
    type = "From",
    d0 = "2012-01-01",
    tol = 0.0000002,
    exact.ml = FALSE,
    unit.root.limit = 0.98
)

Set Outlier Detection Parameters

Description

Function allowing to customize the automatic outlier detection process built in in the pre-processing step (regarima or tramo).

Usage

set_outlier(
  x,
  span.type = c(NA, "All", "From", "To", "Between", "Last", "First", "Excluding"),
  d0 = NULL,
  d1 = NULL,
  n0 = 0,
  n1 = 0,
  outliers.type = NA,
  critical.value = NA,
  tc.rate = NA,
  method = c(NA, "AddOne", "AddAll"),
  maxiter = NA,
  lsrun = NA,
  eml.est = NA
)

Arguments

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()).

If a Seasonal adjustment process is performed, each type of Outlier will be allocated to a pre-defined component after the decomposition: "AO" and "TC" to the irregular, "LS" to the trend and "SO" to seasonal component.

Value

The modified specification (with new outlier parameters)

References

More information on outliers and other auxiliary variables in JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/

See Also

add_outlier, add_usrdefvar

Examples

# Customize a default specification
init_spec <- tramoseats_spec_default
new_spec <- set_outlier(
    x = init_spec,
    span.type = "From",
    d0 = "2012-01-01",
    outliers.type = c("LS", "AO"),
    critical.value = 5,
    tc.rate = 0.85
)

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", "TD2c", "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

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()).

Value

The modified specification (with new trading days variables)

References

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

See Also

modelling_context, calendar_td

Examples

# Pre-defined regressors
y_raw <- ABS$X0.2.09.10.M

# Customize a default specification
init_spec <- x13_spec_default

# Estimation on sub-span between two dates (date d1 is excluded)
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
)

# 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))

## 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)

regs_td <- rjd3toolkit::td(
    s = y_raw,
    groups = c(1, 2, 0, 4, 5, 6, 3),
    contrasts = TRUE
)

variables <- list(
    Monday = regs_td[, 1],
    Tuesday = regs_td[, 2],
    Wednesday = regs_td[, 3],
    Thursday = regs_td[, 4],
    Friday = regs_td[, 5],
    Saturday = regs_td[, 6]
)
# Add regressors to context
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)

Set Log-level Transformation and Decomposition scheme in Pre-Processing Specification

Description

Set Log-level Transformation and Decomposition scheme in Pre-Processing Specification

Usage

set_transform(
  x,
  fun = c(NA, "Auto", "Log", "None"),
  adjust = c(NA, "None", "LeapYear", "LengthOfPeriod"),
  outliers = NA,
  aicdiff = NA,
  fct = NA
)

Arguments

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()).

Value

The modified specification (with log/level transformation scheme)

References

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

See Also

set_outlier, set_tradingdays

Examples

# Customize a default specification
init_spec <- x13_spec_default
new_spec <- set_transform(x = init_spec, fun = "Log", outliers = TRUE)

Set a holiday on a Single Day

Description

Allows to set a holiday as a once-occurring event.

Usage

single_day(date, weight = 1)

Arguments

Value

returns an object of class c("JD3_SINGLEDAY","JD3_HOLIDAY") (with name of the event, date, offset...)

References

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

See Also

national_calendar, fixed_day, special_day,easter_day

Examples

single_day("1999-03-19")

List of Pre-Defined Holidays to choose from

Description

Allows to define a holiday choosing from a list of pre-specified events, equivalent to use fixed_day or easter_day functions.

Usage

special_day(event, offset = 0, weight = 1, validity = NULL)

Arguments

Details

Possible values :

Value

returns an object of class c("JD3_SPECIALDAY","JD3_HOLIDAY") (with name of the event, date, offset...)

References

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

See Also

national_calendar, fixed_day, easter_day

Examples

# To add Easter Monday
special_day("EASTERMONDAY")
# To define a holiday for the day after Christmas, with validity and weight
special_day("CHRISTMAS",
    offset = 1, weight = 0.8,
    validity = list(start = "2000-01-01", end = "2020-12-01")
)

Generic Function For 'JDemetra+' Tests

Description

Generic function to format the results of 'JDemetra+' tests.

Usage

statisticaltest(val, pval, dist = NULL)

## S3 method for class 'JD3_TEST'
print(x, details = FALSE, ...)

Arguments

Value

c("JD3_TEST", "JD3") object that is a list of three parameters:

• value the statistical value of the test.

• pvalue the p-value of the test.

• distribution the statistical distribution used.

Examples

udr_test <- testofupdownruns(random_t(5, 1000))
udr_test # default print
print(udr_test, details = TRUE) # with the distribution

test <- statisticaltest(val = 45, pval = 0.1)
print(test)

Trading day Regressor for Stock series

Description

Allows to generate a specific regressor for correcting trading days effects in Stock series.

Usage

stock_td(frequency, start, length, s, w = 31)

Arguments

Details

The regressor will have the value -1 if the w-th day is a Sunday, 1 if it is a Monday as 0 otherwise.

Value

Time series (object of class c("ts","mts","matrix")).

References

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

See Also

calendar_td

Examples

stock_td(frequency = 12L, start = c(1990L, 1L), length = 480L, w = 1L)

Trading day regressors without holidays

Description

Allows to generate trading day regressors (as many as defined groups), taking into account 7 or less different types of days, from Monday to Sunday, but no specific holidays. Regressors are not corrected for long term mean.

Usage

td(
  frequency,
  start,
  length,
  s,
  groups = c(1, 2, 3, 4, 5, 6, 0),
  contrasts = TRUE
)

Arguments

Details

Aggregated values for monthly or quarterly are the numbers of days belonging to a given group. Contrasts are the differences between the number of days in a given group (1 to 6) and the number of days in the reference group (0).

Value

Time series (object of class c("ts","mts","matrix")) corresponding to each group, starting with the 0-group (contrasts = FALSE) or the 1-group (contrasts = TRUE).

References

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

See Also

calendar_td

Examples

# Monthly regressors for Trading Days: each type of day is different
# contrasts to Sundays (6 series)
regs_td <- td(12, c(2020, 1), 60, groups = c(1, 2, 3, 4, 5, 6, 0), contrasts = TRUE)
# Quarterly regressors for Working Days: week days are similar
# contrasts to week-end days (1 series)
regs_wd <- td(4, c(2020, 1), 60, groups = c(1, 1, 1, 1, 1, 0, 0), contrasts = TRUE)

Canova-Hansen test for stable trading days

Description

Canova-Hansen test for stable trading days

Usage

td_canovahansen(
  s,
  differencing,
  kernel = c("Bartlett", "Square", "Welch", "Tukey", "Hamming", "Parzen"),
  order = NA
)

Arguments

Value

list with the ftest on td, the joint test and the details for the stability of the different days (starting with Mondays).

Examples

s <- log(ABS$X0.2.20.10.M)
td_canovahansen(s, c(1, 12))

Residual Trading Days Test

Description

Residual Trading Days Test

Usage

td_f(
  s,
  model = c("D1", "DY", "DYD1", "WN", "AIRLINE", "R011", "R100"),
  nyears = 0
)

Arguments

Details

The function performs a residual seasonality test that is a joint F-Test on the coefficients of trading days regressors. Several specifications can be used on the model:

• model = "WN" the following model is used:

  y_t - \bar y =\beta TD_t + \varepsilon_t

• model = "D1" (the default) the following model is used:

  \Delta y_t - \overline{\Delta y} =\beta \Delta TD_t + \varepsilon_t

• model = "DY" the following model is used:

  \Delta_s y_t - \overline{\Delta_s y} =\beta \Delta_s TD_t + \varepsilon_t

• model = "DYD1" the following model is used:

  \Delta_s\Delta y_t - \overline{\Delta_s \Delta y} =\beta \Delta_s \Delta TD_t + \varepsilon_t

• model = "AIRLINE" the following model is used:

  y_t =\beta TD_t + \varepsilon_t \text{ with }\varepsilon_t \sim ARIMA(0,1,1)(0,1,1)

• model = "R011" the following model is used:

  y_t =\beta TD_t + \varepsilon_t \text{ with }\varepsilon_t \sim ARIMA(0,1,1)

• model = "R100" the following model is used:

  y_t =\alpha_0 + \alpha_1 y_{t-1} + \beta TD_t + \varepsilon_t

Value

a JD3_TEST object with value, p-value and information about the distribution

Examples

td_f(ABS$X0.2.09.10.M)

Likelihood ratio test on time varying trading days

Description

Likelihood ratio test on time varying trading days

Usage

td_timevarying(s, groups = c(1, 2, 3, 4, 5, 6, 0), contrasts = FALSE)

Arguments

Value

A Chi2 test

Examples

s <- log(ABS$X0.2.20.10.M)
td_timevarying(s)

Creates a time series object

Description

Creates a time series object

Usage

to_ts(source, id, type = "All")

Arguments

Value

An object of type "JD3_TS". List containing the identifiers, the data and the metadata

Examples

source <- "Txt"
# id is split due to length restrictions
id1 <- "demetra://tsprovider/Txt/20111201/SERIES?datePattern=dd%2FMM%2Fyyyy&delimiter=SEMICOLON&"
id2 <- "file=C%3A%5CDocuments%5CIPI%5CData%5CIPI_nace4.csv#seriesIndex=0"
id <- paste0(id1, id2)

to_ts(source, id)

Creates a collection of time series

Description

Creates a collection of time series

Usage

to_tscollection(source, id, type = "All")

Arguments

Value

An object of type "JD3_TSCOLLECTION". List containing the identifiers, the metadata and all the series (data).

Examples

# id is split due to length restrictions
id1 <- "demetra://tsprovider/Txt/20111201/SERIES?datePattern=dd%2FMM%2Fyyyy&delimiter=SEMICOLON&"
id2 <- "file=C%3A%5CDocuments%5CIPI%5CData%5CIPI_nace4.csv#seriesIndex=0"
id <- paste0(id1, id2)
source <- "Txt"
my_collection <- to_tscollection(source, id)

Default Tramo-Seats specification

Description

Default Tramo-Seats specification

Usage

tramoseats_spec_default

Format

An object of class JD3_TRAMOSEATS_SPEC of length 3.

Examples

data(tramoseats_spec_default)

Trigonometric variables

Description

Computes trigonometric variables at different frequencies.

Usage

trigonometric_variables(frequency, start, length, s, seasonal_frequency = NULL)

Arguments

Details

Denote by P the value of frequency (= the period) and f_1, ..., f_n the frequencies provides by seasonal_frequency (if seasonal_frequency = NULL then n=\lfloor P/2\rfloor and f_i=i).

trigonometric_variables returns a matrix of size length\times(2n).

For each date t associated to the period m (m\in[1,P]), the columns 2i and 2i-1 are equal to:

\cos \left( \frac{2 \pi}{P} \times m \times f_i \right) \text{ and } \sin \left( \frac{2 \pi}{P} \times m \times f_i \right)

Take for example the case when the first date (date) is a January, frequency = 12 (monthly time series), length = 12 and seasonal_frequency = NULL. The first frequency, \lambda_1 = 2\pi /12 represents the fundamental seasonal frequency and the other frequencies (\lambda_2 = 2\pi /12 \times 2, ..., \lambda_6 = 2\pi /12 \times 6) are the five harmonics. The output matrix will be equal to:

\begin{pmatrix} \cos(\lambda_1) & \sin (\lambda_1) & \cdots & \cos(\lambda_6) & \sin (\lambda_6) \\ \cos(\lambda_1\times 2) & \sin (\lambda_1\times 2) & \cdots & \cos(\lambda_6\times 2) & \sin (\lambda_6\times 2)\\ \vdots & \vdots & \cdots & \vdots & \vdots \\ \cos(\lambda_1\times 12) & \sin (\lambda_1\times 12) & \cdots & \cos(\lambda_6\times 12) & \sin (\lambda_6\times 12) \end{pmatrix}

Value

a mts object with 2 columns

Examples

trigonometric_variables(
    frequency = 12,
    length = 480,
    start = c(1990, 1),
    seasonal_frequency = 3
)

Multiplicative adjustment of a time series for leap year / length of periods

Description

Multiplicative adjustment of a time series for leap year / length of periods

Usage

ts_adjust(s, method = c("LeapYear", "LengthOfPeriod"), reverse = FALSE)

Arguments

Value

The interpolated series

Examples

y <- ABS$X0.2.09.10.M
ts_adjust(y)
# with reverse we can find the
all.equal(ts_adjust(ts_adjust(y), reverse = TRUE), y)

Interpolation of a time series with missing values

Description

Interpolation of a time series with missing values

Usage

ts_interpolate(s, method = c("airline", "average"))

Arguments

Value

The interpolated series

Examples

ts_interpolate(AirPassengers)

x <- AirPassengers
x[50:60] <- NA
ts_interpolate(x)

Create ts object with values and dates

Description

Create ts object with values and dates

Usage

tsdata_of(values, dates)

Arguments

Value

A ts object. The frequency will be identified automatically and missing values will be added in need be. The identified frequency will be the lowest frequency that match the figures. The provided data can contain missing values (NA)

Examples

# Annual series
s <- tsdata_of(c(1, 2, 3, 4), c("1990-01-01", "1995-01-01", "1996-01-01",
        "2000-11-01"))
# Quarterly series
t <- tsdata_of(c(1, 2, 3, NA, 4), c("1990-01-01", "1995-01-01", "1996-01-01",
        "2000-08-01", "2000-11-01"))

Makes a UCARIMA model canonical

Description

More specifically, put all the noise of the components in one dedicated component

Usage

ucarima_canonical(ucm, cmp = 0, adjust = TRUE)

Arguments

Value

A new UCARIMA model

Examples

mod1 <- arima_model("trend", delta = c(1, -2, 1))
mod2 <- arima_model("noise", var = 1600)
hp <- ucarima_model(components = list(mod1, mod2))
hpc <- ucarima_canonical(hp, cmp = 2)

Estimate UCARIMA Model

Description

Estimate UCARIMA Model

Usage

ucarima_estimate(x, ucm, stdev = TRUE)

Arguments

Value

A matrix containing the different components and their standard deviations if stdev is TRUE.

Examples

mod1 <- arima_model("trend", delta = c(1, -2, 1))
mod2 <- arima_model("noise", var = 16)
hp <- ucarima_model(components = list(mod1, mod2))
s <- log(aggregate(Retail$AutomobileDealers))
all <- ucarima_estimate(s, hp, stdev = TRUE)
plot(s, type = "l")
t <- ts(all[, 1], frequency = frequency(s), start = start(s))
lines(t, col = "blue")

Creates an UCARIMA model, which is composed of ARIMA models with independent innovations.

Description

Creates an UCARIMA model, which is composed of ARIMA models with independent innovations.

Usage

ucarima_model(model = NULL, components, complements = NULL, checkmodel = FALSE)

Arguments

Value

A list with the reduced model, the components and their complements

Examples

mod1 <- arima_model("trend", delta = c(1, -2, 1))
mod2 <- arima_model("noise", var = 1600)
hp <- ucarima_model(components = list(mod1, mod2))
print(hp$model)

Wiener Kolmogorov Estimators

Description

Wiener Kolmogorov Estimators

Usage

ucarima_wk(ucm, cmp, signal = TRUE, nspectrum = 601, nwk = 300)

Arguments

Value

A list with the (pseudo-)spectrum, the weights of the filter and the squared-gain function (with the same number of points as the spectrum)

Examples

mod1 <- arima_model("trend", delta = c(1, -2, 1))
mod2 <- arima_model("noise", var = 1600)
hp <- ucarima_model(components = list(mod1, mod2))
wk1 <- ucarima_wk(hp, 1, nwk = 50)
wk2 <- ucarima_wk(hp, 2)
plot(wk1$filter, type = "h")

Create a Composite Calendar

Description

Allows to combine two or more calendars into one calendar, weighting all the holidays of each of them.

Usage

weighted_calendar(calendars, weights)

Arguments

Details

Composite calendars are useful for a series that including data from more than one country/region. They can be used, for example, to create the calendar for the European Union or to create the national calendar for a country, in which regional holidays are celebrated. For example, in Germany public holidays are determined by the federal states. Therefore, Epiphany is celebrated only in Baden-Wurttemberg, Bavaria and in Saxony-Anhalt, while from 1994 Day of Repentance and Prayer is celebrated only in Saxony.

Value

returns an object of class c("JD3_WEIGHTEDCALENDAR", "JD3_CALENDARDEFINITION")

References

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

See Also

national_calendar, chained_calendar

Examples

Belgium <- national_calendar(list(special_day("NEWYEAR"), fixed_day(7, 21)))
France <- national_calendar(list(special_day("NEWYEAR"), fixed_day(7, 14)))
composite_calendar <- weighted_calendar(list(France, Belgium), weights = c(1, 2))

Default X13 specification

Description

Default X13 specification

Usage

x13_spec_default

Format

An object of class JD3_X13_SPEC of length 3.

Examples

data(x13_spec_default)