Package 'rjd3tramoseats'

Deprecated functions

Description

Deprecated functions

Usage

fast_tramoseats(
  ts,
  spec = c("rsafull", "rsa0", "rsa1", "rsa2", "rsa3", "rsa4", "rsa5"),
  context = NULL,
  userdefined = NULL
)

fast_tramo(
  ts,
  spec = c("trfull", "tr0", "tr1", "tr2", "tr3", "tr4", "tr5"),
  context = NULL,
  userdefined = NULL
)

spec_tramoseats(
  name = c("rsafull", "rsa0", "rsa1", "rsa2", "rsa3", "rsa4", "rsa5")
)

spec_tramo(name = c("trfull", "tr0", "tr1", "tr2", "tr3", "tr4", "tr5"))

userdefined_variables_tramoseats(x = c("TRAMO-SEATS", "TRAMO"))

Arguments

ts	a univariate time series.
spec	the model specification. Can be either the name of a predefined specification or a user-defined specification.
context	the dictionary of variables.
userdefined	a vector containing the additional output variables (see tramoseats_dictionary()).
name	the name of a predefined specification.
x	useless parameter

Value

All these functions are deprecated and return the same value as the function that replaces them:

• spec_tramoseats() returns the same value as tramoseats_spec()

• spec_tramo() returns the same value as tramo_spec()

• fast_tramoseats() returns the same value as tramoseats_fast()

• fast_regarima() returns the same value as regarima_fast()

• .jtramoseats() returns the same value as jtramoseats()

• userdefined_variables_tramoseats() returns the same value as tramoseats_dictionary()

---

Java Utility Functions

Description

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

Usage

.tramoseats_rslts(jrslts)

.jd2r_spec_tramo(jspec)

.r2jd_spec_tramo(spec)

.jd2r_spec_tramoseats(jspec)

.r2jd_spec_tramoseats(spec)

Arguments

spec, jspec, jrslts

parameters.

Value

These functions return specification in Java, proto or R.

---

Refresh a specification with constraints

Description

Functions tramoseats_refresh and tramo_refresh allow to create a new specification by updating an existing one. Some selected parameters will be kept fixed while others will be freed within the boundaries of a reference specification. In practice each freed parameter of the specification to be updated (spec) is replaced by the corresponding parameter of the reference specification (refspec). See details and examples.

Usage

tramo_refresh(
  spec,
  refspec = NULL,
  policy = c("FreeParameters", "Complete", "Outliers_StochasticComponent", "Outliers",
    "FixedParameters", "FixedAutoRegressiveParameters", "Fixed", "Current"),
  period = 0,
  start = NULL,
  end = NULL
)

tramoseats_refresh(
  spec,
  refspec = NULL,
  policy = c("FreeParameters", "Complete", "Outliers_StochasticComponent", "Outliers",
    "FixedParameters", "FixedAutoRegressiveParameters", "Fixed", "Current"),
  period = 0,
  start = NULL,
  end = NULL
)

Arguments

spec	specification to be refreshed Object of class "JD3_tramoseats_SPEC" or "JD3_tramo_SPEC", can be obtained as an output of tramoseats_spec or tramo_spec and customised with set_ functions, see tramoseats_spec documentation
refspec	reference specification By default "rsa4" or "tr5" specification. Object of class "JD3_tramoseats_SPEC" or "JD3_tramo_SPEC", can be obtained as an output of tramoseats_spec or tramo_spec and customised with set_ functions, see tramoseats_spec documentation
policy	refresh policy to apply (see details)
period, start, end	additional parameters used to specify the span when policy = "Current" or policy = "Outliers" or policy = "Outliers_StochasticComponent" period: numeric, number of observations in a year (12, 4...), compulsory, if false or missing, re-estimation with refreshed specification won't work. When policy = "Outliers" or policy = "Outliers_StochasticComponent" start has to be specified as the date from which outliers will be re-identified end is not used, if specified it will be ignored. When policy = "Current" start and end have to be both specified and indicate the span on which additive outliers (AO) will be added. Span definition: start and end: defined as arrays of two elements: year and first period (for example, period = 12 and start=c(1980, 1) stands for January 1980)

Details

A particular selection of parameters to be kept fixed or re-estimated is called a revision policy.

Available refresh policies are:

1. Current: applying the current pre-adjustment reg-arima model and handling the new raw data points, or any sub-span of the series as Additive Outliers (defined as new intervention variables); SEATS and Benchmarking part parameters are untouched.

2. Fixed: applying the current pre-adjustment reg-arima model and replacing forecasts by new raw data points; SEATS and Benchmarking part parameters are untouched.

3. FixedParameters: pre-adjustment reg-arima model is partially modified: regression coefficients will be re-estimated but regression variables, Arima orders and coefficients are unchanged;

4. FixedAutoRegressiveParameters: same as FixedParameters but Arima Moving Average coefficients (MA) are also re-estimated, Auto-regressive (AR) coefficients are kept fixed; SEATS and Benchmarking part parameters are untouched.

5. FreeParameters: all regression and Arima model coefficients are re-estimated, regression variables and Arima orders are kept fixed; SEATS and Benchmarking part parameters are untouched.

6. Outliers: regression variables and Arima orders are kept fixed, but outliers will be re-detected on the defined span, thus all regression and Arima model coefficients are re-estimated; SEATS and Benchmarking part parameters are untouched.

7. Outliers_StochasticComponent: same as "Outliers" but Arima model orders (p,d,q)(P,D,Q) can also be re-identified; SEATS and Benchmarking part parameters are untouched.

8. Complete: All the parameters are re-identified and re-estimated, unless constrained in the domain spec. SEATS and Benchmarking part parameters are entirely reset to values in the reference spec.

Value

a new specification, an object of class "JD3_tramoseats_SPEC" or "JD3_tramo_SPEC".

References

More information on revision policies in JDemetra+ documentation: https://doc.jdemetra.org/a-rev-policies

Examples

library("rjd3toolkit")
y <- rjd3toolkit::ABS$X0.2.08.10.M
# raw series for first estimation
y_raw <- window(y, end = c(2016, 12))
# raw series for second (refreshed) estimation: new data points
y_new <- window(y, end = c(2017, 6))

# Example 1 : refresh mechanism
# Create reference spec, here the default "rsa3"
rsa3<- tramoseats_spec("rsa3")
# Customize this spec
## Tramo part
### For example, disable automatic arima modelling
user_spec <- set_automodel(rsa3, enabled = FALSE)
### set a user-defined arima model
user_spec <- set_arima(
   user_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")
)
#print(user_spec)

## Customize the x11 part
user_spec<-set_seats(user_spec,
                    fcasts = -2,
                    bcasts = -1,
                    trend.boundary=0.6,
                    seas.boundary=0.75,
                    algorithm= "KalmanSmoother")
#print(user_spec)
user_spec<- set_benchmarking(
   user_spec,
   enabled = TRUE,
   target = "Original",
   rho = 0.7,
   lambda = 0.5,
   forecast = TRUE,
   bias = "Multiplicative")
#print(user_spec)
# Use policy: "Outliers_StochasticComponent"
tramoseats_spec_ref <- tramoseats_refresh(spec= user_spec,
                           refspec= rsa3,
                           policy = "Outliers_StochasticComponent"
)
# print(tramoseats_spec_ref)
# user defined reg-arima model is reset and outliers will be re-identified
# on the whole series as no start and end specified, SEATS and Benchmarking parameters
# are left unchanged
# Use policy: "Complete"
tramoseats_spec_ref <- tramoseats_refresh(spec= user_spec,
                           refspec= rsa3,
                           policy = "Complete"
)
# print(tramoseats_spec_ref)
# all user defined parameters are reset and replaced with "rsa3" parameters,
# Example 2 : practical re-estimation use-case
sa_tramoseats <- tramoseats(y_raw, user_spec)
# refreshing the specification resulting from the first estimation
# to partially adapt to new data
spec_to_refresh <- sa_tramoseats$result_spec
reference_spec <- sa_tramoseats$estimation_spec
# policy = "Fixed"
spec_tramoseats_ref <- tramoseats_refresh(spec_to_refresh,
                            reference_spec,
                            policy = "Fixed"
)
# 2nd estimation with refreshed specification
sa_tramoseats_ref <- tramoseats(y_new, spec_tramoseats_ref)
# policy = "Outliers"
spec_tramoseats_ref <- tramoseats_refresh(spec_to_refresh,
                           reference_spec,
                           policy = "Outliers",
                           period = 12,
                           start = c(2017, 1)
)
# outliers will be re-detected from January 2017 included
# 2nd estimation with refreshed specification
sa_tramoseats_ref <- tramoseats(y_new, spec_tramoseats_ref)

# policy = "Current"

spec_tramoseats_ref <- tramoseats_refresh(spec_to_refresh,
                           reference_spec,
                           policy = "Current",
                           period = 12,
                           start = c(2017, 1),
                           end = end(y_new)
)
sa_tramoseats_ref <- tramoseats(y_new, spec_tramoseats_ref)

# Points from January 2017 (included) until the end of the series will be
# treated as Additive Outliers, the previous reg-Arima model being otherwise
# kept fixed 2nd estimation with refreshed specification

---

SEATS Decomposition

Description

SEATS Decomposition

Usage

seats_decompose(
  sarima,
  seas.tolerance = 2,
  trend.boundary = 0.5,
  seas.boundary = 0.8,
  seas.boundary.unique = 0.8,
  approximation = c("None", "Legacy", "Noisy")
)

Arguments

sarima	SARIMA model (see rjd3toolkit::sarima_model()).
seas.tolerance	numeric: the seasonal tolerance (epsphi). The tolerance (measured in degrees) to allocate the AR non-real roots to the seasonal component (if the modulus of the inverse complex AR root is greater than the trend boundary and the frequency of this root differs from one of the seasonal frequencies by less than Seasonal tolerance) or the transitory component (otherwise). Possible values in [0,10]. Default value 2.
trend.boundary	numeric: the trend boundary (rmod). The boundary beyond which an AR root is integrated in the trend component. If the modulus of the inverse real root is greater than the trend boundary, the AR root is integrated in the trend component. Below this value, the root is integrated in the transitory component. Possible values [0,1]. Default = 0.5.
seas.boundary	numeric: the seasonal boundary (sbound). The boundary beyond which a real negative AR root is integrated in the seasonal component. If the modulus of the inverse negative real root is greater (or equal) than Seasonal boundary, the AR root is integrated into the seasonal component. Otherwise the root is integrated into the trend or transitory component. Possible values [0,1]. Default=0.8.
seas.boundary.unique	numeric: the seasonal boundary (unique), (sboundatpi). The boundary beyond which a negative AR root is integrated in the seasonal component, when the root is the unique seasonal root. If the modulus of the inverse negative real root is greater (or equal) than Seasonal boundary, the AR root is integrated into the seasonal component. Otherwise the root is integrated into the trend or transitory component. Possible values [0,1]. Default=0.8.
approximation	character: the approximation mode. When the ARIMA model estimated by TRAMO does not accept an admissible decomposition, SEATS: "None" - performs an approximation; "Legacy" - replaces the model with a decomposable one; "Noisy" - estimates a new model by adding a white noise to the non-admissible model estimated by TRAMO. Default="Legacy".

Value

returns a "JD3_UCARIMA" object

Examples

seats_decompose(rjd3toolkit::sarima_model(period = 12, phi = c(0, 1), bd = 1))

---

Set Seats Specification

Description

Function allowing to customize parameters in the decomposition part (Seats) of a Tramo-Seats seasonal adjustment process. (Seats is an Arima Model Based decomposition algorithm working in conjunction with Tramo.)

Usage

set_seats(
  x,
  approximation = c(NA, "None", "Legacy", "Noisy"),
  trend.boundary = NA,
  seas.boundary = NA,
  seas.boundary.unique = NA,
  seas.tolerance = NA,
  ma.boundary = NA,
  fcasts = NA,
  bcasts = NA,
  algorithm = c(NA, "Burman", "KalmanSmoother"),
  bias = NA
)

Arguments

x	the specification to be modified, object of class "JD3_TRAMOSEATS_SPEC", has to be generated with tramoseats_spec() function
approximation	character: the approximation mode. When the ARIMA model estimated by TRAMO does not accept an admissible decomposition, SEATS: "None" - performs an approximation; "Legacy" - replaces the model with a decomposable one; "Noisy" - estimates a new model by adding a white noise to the non-admissible model estimated by TRAMO. Default="Legacy".
trend.boundary	numeric: the trend boundary (rmod). The boundary beyond which an AR root is integrated in the trend component. If the modulus of the inverse real root is greater than the trend boundary, the AR root is integrated in the trend component. Below this value, the root is integrated in the transitory component. Possible values [0,1]. Default = 0.5.
seas.boundary	numeric: the seasonal boundary (sbound). The boundary beyond which a real negative AR root is integrated in the seasonal component. If the modulus of the inverse negative real root is greater (or equal) than Seasonal boundary, the AR root is integrated into the seasonal component. Otherwise the root is integrated into the trend or transitory component. Possible values [0,1]. Default=0.8.
seas.boundary.unique	numeric: the seasonal boundary (unique), (sboundatpi). The boundary beyond which a negative AR root is integrated in the seasonal component, when the root is the unique seasonal root. If the modulus of the inverse negative real root is greater (or equal) than Seasonal boundary, the AR root is integrated into the seasonal component. Otherwise the root is integrated into the trend or transitory component. Possible values [0,1]. Default=0.8.
seas.tolerance	numeric: the seasonal tolerance (epsphi). The tolerance (measured in degrees) to allocate the AR non-real roots to the seasonal component (if the modulus of the inverse complex AR root is greater than the trend boundary and the frequency of this root differs from one of the seasonal frequencies by less than Seasonal tolerance) or the transitory component (otherwise). Possible values in [0,10]. Default value 2.
ma.boundary	numeric: the MA unit root boundary. When the modulus of an estimated MA root falls in the range [xl, 1], it is set to xl. Possible values [0.9,1]. Default=0.95.
bcasts, fcasts	numeric: the number of backasts (bcasts) or forecasts (fcasts) used in the decomposition in periods (positive values) or years (negative values). Default bcasts = 0. Default fcasts = 0.
algorithm	character: the estimation method for the unobserved components. The choice can be made from: Burman: the default value. May result in a significant underestimation of the components' standard deviation, as it may become numerically unstable when some roots of the MA polynomial are near 1; KalmanSmoother: it is not disturbed by the (quasi-) unit roots in MA.
bias	boolean. If TRUE (default) and decomposition is multiplicative a correction is applied when computing final components (S, T, I) in level from components estimated in log. Average of S (on complete years) and I (on the whole series) is set to 1, the correction is allocated to T.

Value

an object of class "JD3_TRAMOSEATS_SPEC".

References

More information and examples related to 'JDemetra+' features in the online documentation: https://jdemetra-new-documentation.netlify.app/

See Also

tramoseats_spec().

Examples

init_spec <- tramoseats_spec("rsafull")
new_spec <- set_seats(init_spec,
    approximation = "Legacy",
    trend.boundary = 0.8,
    seas.boundary = 0.5,
    fcasts = -3,
    algorithm = "KalmanSmoother",
    bias = TRUE
)
y <- rjd3toolkit::ABS$X0.2.09.10.M

sa <- tramoseats(y, spec = new_spec)

---

TERROR Quality Control of Outliers

Description

TRAMO for ERRORs (TERROR) controls the quality of the data by checking outliers at the end of the series

Usage

terror(
  ts,
  spec = c("trfull", "tr0", "tr1", "tr2", "tr3", "tr4", "tr5"),
  nback = 1,
  context = NULL
)

Arguments

ts	a univariate time series.
spec	the model specification. Can be either the name of a predefined specification or a user-defined specification.
nback	number of last observations considered for the quality check.
context	the dictionary of variables.

Value

a mts object with 7 variables:

1. actual: the actual data at the end of the series;

2. forecast: the forecast of the actual data at the end of the series;

3. error: the absolute errors (= observed - forecasts);

4. rel.error: relative errors ("scores") : ratios between the forecast errors and the standard deviation of the forecasts of the last observations (positive values mean under-estimation);

5. raw: the transformed series. More especially, if the chosen model implies a log-transformation, the values are obtained after a log-transformation. Other transformations, such leap year corrections or length-of periods corrections may also be used;

6. fraw: the forecast of the transformed series.;

7. efraw: the absolute errors of the transformed series.

Examples

terror(rjd3toolkit::ABS$X0.2.09.10.M, nback = 2)

---

TRAMO model, pre-adjustment in TRAMO-SEATS

Description

allows to model the series with a Reg-Arima model, estimate outlier, calendar or other regression effects and produce forecasts

Usage

tramo(
  ts,
  spec = c("trfull", "tr0", "tr1", "tr2", "tr3", "tr4", "tr5"),
  context = NULL,
  userdefined = NULL
)

tramo_fast(
  ts,
  spec = c("trfull", "tr0", "tr1", "tr2", "tr3", "tr4", "tr5"),
  context = NULL,
  userdefined = NULL
)

Arguments

ts	a univariate time series.
spec	the model specification. Can be either the name of a predefined specification or a user-defined specification.
context	the dictionary of variables.
userdefined	a vector containing the additional output variables (see tramoseats_dictionary()).

Value

the tramo() function returns a list with the results ("JD3_tramo_rslts" object), the estimation specification and the result specification, while tramo_fast() is a faster function that only returns the results.

Examples

library("rjd3toolkit")

y <- rjd3toolkit::ABS$X0.2.09.10.M
sp <- tramo_spec("trfull")
sp <- add_outlier(sp,
    type = c("AO"), c("2015-01-01", "2010-01-01")
)

tramo_fast(y, spec = sp)

sp <- set_transform(
    set_tradingdays(
        set_easter(sp, enabled = FALSE),
        option = "workingdays"
    ),
    fun = "None"
)

tramo_fast(y, spec = sp)

sp <- set_outlier(sp, outliers.type = c("AO"))


tramo_fast(y, spec = sp)

---

Forecasts with TRAMO

Description

Forecasts with TRAMO

Usage

tramo_forecast(
  ts,
  spec = c("trfull", "tr0", "tr1", "tr2", "tr3", "tr4", "tr5"),
  nf = -1,
  context = NULL
)

Arguments

ts	a univariate time series.
spec	the model specification. Can be either the name of a predefined specification or a user-defined specification.
nf	the forecasting horizon (numeric). The forecast length is in periods (positive values) or years (negative values). By default, the program generates a one-year forecast (nf = -1).
context	the dictionary of variables.

Value

a mts object with 7 variables:

• forecast the forecast of the actual data at the end of the series.

• error standard deviation of the forecast.

• fraw the forecast of the transformed series.

• efraw the standard deviation of the forecast of the transformed series.

Examples

tramo_forecast(rjd3toolkit::ABS$X0.2.09.10.M)

---

Outlier Detection with a Tramo Model

Description

Tramo is a particular regarima model estimation algorithm, mainly used to linearized the series before performing a decomposition with Seats

Usage

tramo_outliers(
  y,
  order = c(0L, 1L, 1L),
  seasonal = c(0L, 1L, 1L),
  mean = FALSE,
  X = NULL,
  X.td = NULL,
  ao = TRUE,
  ls = TRUE,
  tc = FALSE,
  so = FALSE,
  cv = 0,
  ml = FALSE,
  clean = FALSE
)

Arguments

y	the dependent variable (a ts object).
order, seasonal	the orders of the ARIMA model.
mean	Boolean to include or not the mean.
X	user defined regressors (other than calendar).
X.td	calendar regressors.
ao, ls, so, tc	Boolean to indicate which type of outliers should be detected.
cv	numeric. The entered critical value for the outliers' detection procedure. If equal to 0 the critical value for the outliers' detection procedure is automatically determined by the number of observations.
ml	Use of maximum likelihood (otherwise approximation by means of Hannan-Rissanen).
clean	Clean missing values at the beginning/end of the series. Regression variables are automatically resized, if need be.

Value

a "JD3_REGARIMA_OUTLIERS" object.

Examples

tramo_outliers(rjd3toolkit::ABS$X0.2.09.10.M)

---

Seasonal Adjustment with TRAMO-SEATS

Description

Seasonal Adjustment with TRAMO-SEATS

Usage

tramoseats(
  ts,
  spec = c("rsafull", "rsa0", "rsa1", "rsa2", "rsa3", "rsa4", "rsa5"),
  context = NULL,
  userdefined = NULL
)

tramoseats_fast(
  ts,
  spec = c("rsafull", "rsa0", "rsa1", "rsa2", "rsa3", "rsa4", "rsa5"),
  context = NULL,
  userdefined = NULL
)

.jtramoseats(
  ts,
  spec = c("rsafull", "rsa0", "rsa1", "rsa2", "rsa3", "rsa4", "rsa5"),
  context = NULL,
  userdefined = NULL
)

Arguments

ts	a univariate time series.
spec	the model specification. Can be either the name of a predefined specification or a user-defined specification.
context	the dictionary of variables.
userdefined	a vector containing the additional output variables (see tramoseats_dictionary()).

Value

The tramoseats() function returns a list with the results, the estimation specification and the result specification, while tramoseats_fast() is a faster function that only returns the results. The .jtramoseats() functions only results the java object to custom outputs in other packages (use rjd3toolkit::dictionary() to get the list of variables and rjd3toolkit::result() to get a specific variable).

Examples

library("rjd3toolkit")

sp <- tramoseats_spec("rsafull")
y <- rjd3toolkit::ABS$X0.2.09.10.M

tramoseats(y, spec = sp)
tramoseats_fast(y, spec = sp)

sp <- add_outlier(sp,
    type = c("AO"), c("2015-01-01", "2010-01-01")
)
sp <- set_transform(
    set_tradingdays(
        set_easter(sp, enabled = FALSE),
        option = "workingdays"
    ),
    fun = "None"
)

tramoseats(y, spec = sp)
tramoseats_fast(y, spec = sp)

---

TRAMO-SEATS dictionary

Description

Functions to provide information for all output objects (series, diagnostics, parameters) available with tramoseats() function.

Usage

tramoseats_dictionary()

tramoseats_full_dictionary()

Details

These functions provide lists of output names (series, diagnostics, parameters) available with the tramoseats() function. These names can be used to generate customized outputs with the userdefined option of the tramoseats() function (see examples). The tramoseats_full_dictionary function provides additional information on object format and description.

Value

tramoseats_dictionary() returns a character vector containing the names of all output objects (series, diagnostics, parameters) available with the tramoseats() function, whereas tramoseats_full_dictionary() returns a data.frame with format and description, for all the output objects.

Examples

# Visualize the dictionary
print(tramoseats_dictionary())
summary(tramoseats_dictionary())

# first 10 lines
head(tramoseats_full_dictionary(), n = 10)
# For more structured information call `View(tramoseats_full_dictionary())`

# Extract names of output of interest
user_defined_output <- tramoseats_dictionary()[c(65, 95, 135)]
user_defined_output

# Generate the corresponding output in an estimation
y <- rjd3toolkit::ABS$X0.2.09.10.M

m <- tramoseats(y, "rsafull", userdefined=user_defined_output)

# Retrieve user defined output
tail(m$user_defined$ylin)
m$user_defined$residuals.kurtosis
m$user_defined$sa_f

---

Revisions History

Description

Computes revisions history

Usage

tramoseats_revisions(
  ts,
  spec,
  data_ids = NULL,
  ts_ids = NULL,
  cmp_ids = NULL,
  context = NULL
)

Arguments

ts	The time series used for the estimation.
spec	The specification used.
data_ids	A list of list to specify the statistics to export. Each sub-list must contain two elements: start (first date to compute the history, in the format "YYYY-MM-DD") and id (the name of the statistics, see tramoseats_dictionary()). See example.
ts_ids	A list of list to specify the specific date of a component whose history is to be studied. Each sub-list must contain three elements: start (first date to compute the history, in the format "YYYY-MM-DD"), period (the date of the studied) and id (the name of the component, see tramoseats_dictionary()). See example.
cmp_ids	A list of list to specify the component whose history is to be studied. Each sub-list must contain three elements: start (first date to compute the history, in the format "YYYY-MM-DD"), end (last date to compute the history, in the format "YYYY-MM-DD") and id (the name of the component, see tramoseats_dictionary()). As many series as periods between start and end will be exported. See example.
context	The context of the specification.

Value

returns a list

Examples

s <- rjd3toolkit::ABS$X0.2.09.10.M
sa_mod <- tramoseats(s)
data_ids <- list(
    # Get the coefficient of the trading-day coefficient from 2005-jan
    list(start = "2005-01-01", id = "regression.td(1)"),
    # Get the ljung-box statistics on residuals from 2010-jan
    list(start = "2010-01-01", id = "residuals.lb")
)
ts_ids <- list(
    # Get the SA component estimates of 2010-jan from 2010-jan
    list(period = "2010-01-01", start = "2010-01-01", id = "sa"),
    # Get the irregular component estimates of 2010-jan from 2015-jan
    list(period = "2010-01-01", start = "2015-01-01", id = "i")
)
cmp_ids <- list(
    # Get the SA component estimates (full time series) 2010-jan to 2020-jan
    list(start = "2010-01-01", end = "2020-01-01", id = "sa"),
    # Get the trend component estimates (full time series)  2010-jan to 2020-jan
    list(start = "2010-01-01", end = "2020-01-01", id = "t")
)
rh <- tramoseats_revisions(s, sa_mod$result_spec, data_ids, ts_ids, cmp_ids)

---

TRAMO/TRAMO-SEATS Default Specification

Description

Set of functions(tramoseats_spec(),tramo_spec()) to create default specifications associated with the TRAMO-SEATS seasonal adjustment method. Specification creation can be restricted to the tramo part with the tramo_spec() function.

Usage

tramo_spec(name = c("trfull", "tr0", "tr1", "tr2", "tr3", "tr4", "tr5"))

tramoseats_spec(
  name = c("rsafull", "rsa0", "rsa1", "rsa2", "rsa3", "rsa4", "rsa5")
)

Arguments

name	the name of a predefined specification.

Details

Without argument tramo_spec() yields a TR5 specification

without argument tramoseats_spec() yields a RSA5 specification

The available predefined 'JDemetra+' model specifications are described in the table below:

Identifier |	Log/level detection |	Outliers detection |	Calendar effects |	ARIMA
RSA0/TR0 |	NA |	NA |	NA |	Airline(+mean)
RSA1/TR1 |	automatic |	AO/LS/TC |	NA |	Airline(+mean)
RSA2/TR2 |	automatic |	AO/LS/TC |	2 td vars + Easter |	Airline(+mean)
RSA3/TR3 |	automatic |	AO/LS/TC |	NA |	automatic
RSA4/TR4 |	automatic |	AO/LS/TC |	2 td vars + Easter |	automatic
RSA5/TR5 |	automatic |	AO/LS/TC |	7 td vars + Easter |	automatic
RSAfull/TRfull |	automatic |	AO/LS/TC |	automatic |	automatic

Value

an object of class "JD3_TRAMOSEATS_SPEC" (tramoseats_spec()) or "JD3_TRAMO_SPEC" (tramo_spec()).

See Also

1. To set the pre-processing parameters: rjd3toolkit::set_arima(), rjd3toolkit::set_automodel(), rjd3toolkit::set_basic(), rjd3toolkit::set_easter(), rjd3toolkit::set_estimate(), rjd3toolkit::set_outlier(), rjd3toolkit::set_tradingdays(), rjd3toolkit::set_transform(), rjd3toolkit::add_outlier(), rjd3toolkit::remove_outlier(), rjd3toolkit::add_ramp(), rjd3toolkit::remove_ramp(), rjd3toolkit::add_usrdefvar();

2. To set the decomposition parameters: set_seats();

3. To set the benchmarking parameters: rjd3toolkit::set_benchmarking().

Examples

init_spec <- tramoseats_spec()
init_spec <- tramo_spec()
init_spec <- tramoseats_spec("rsa3")
init_spec <- tramo_spec("tr3")

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