Title: | Seasonal Adjustment with X-13 in 'JDemetra+ 3.x' |
---|---|
Description: | R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It offers full acces to options and outputs of X-13, including RegARIMA modelling (automatic ARIMA model with outlier detection and trading days adjustment) and X-11 decomposition. |
Authors: | Jean Palate [aut], Alain Quartier-la-Tente [aut] , Tanguy Barthelemy [aut, cre, art], Anna Smyk [aut] |
Maintainer: | Tanguy Barthelemy <[email protected]> |
License: | file LICENSE |
Version: | 3.3.0 |
Built: | 2024-10-28 18:18:35 UTC |
Source: | https://github.com/rjdverse/rjd3x13 |
Deprecated functions
spec_x13(name = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c")) spec_regarima(name = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c")) spec_x11() fast_x13( ts, spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"), context = NULL, userdefined = NULL ) fast_regarima( ts, spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"), context = NULL, userdefined = NULL )
spec_x13(name = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c")) spec_regarima(name = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c")) spec_x11() fast_x13( ts, spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"), context = NULL, userdefined = NULL ) fast_regarima( ts, spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"), context = NULL, userdefined = NULL )
ts , spec , context , userdefined , name
|
Parameters. |
These functions are used in all JDemetra+ 3.0 packages to easily interact between R and Java objects.
.x13_rslts(jrslts) .jd2r_spec_x11(jspec) .r2jd_spec_x11(spec) .r2jd_spec_regarima(spec) .jd2r_spec_regarima(jspec) .r2jd_spec_x13(spec) .jd2r_spec_x13(jspec)
.x13_rslts(jrslts) .jd2r_spec_x11(jspec) .r2jd_spec_x11(spec) .r2jd_spec_regarima(spec) .jd2r_spec_regarima(jspec) .r2jd_spec_x13(spec) .jd2r_spec_x13(jspec)
spec , jspec , jrslts
|
parameters. |
Function allowing to create a new specification by updating a specification used for a previous estimation. Some selected parameters will be kept fixed (previous estimation results) while others will be freed for re-estimation in a domain of constraints. See details and examples.
regarima_refresh( spec, refspec = NULL, policy = c("FreeParameters", "Complete", "Outliers_StochasticComponent", "Outliers", "FixedParameters", "FixedAutoRegressiveParameters", "Fixed", "Current"), period = 0, start = NULL, end = NULL ) x13_refresh( spec, refspec = NULL, policy = c("FreeParameters", "Complete", "Outliers_StochasticComponent", "Outliers", "FixedParameters", "FixedAutoRegressiveParameters", "Fixed", "Current"), period = 0, start = NULL, end = NULL )
regarima_refresh( spec, refspec = NULL, policy = c("FreeParameters", "Complete", "Outliers_StochasticComponent", "Outliers", "FixedParameters", "FixedAutoRegressiveParameters", "Fixed", "Current"), period = 0, start = NULL, end = NULL ) x13_refresh( spec, refspec = NULL, policy = c("FreeParameters", "Complete", "Outliers_StochasticComponent", "Outliers", "FixedParameters", "FixedAutoRegressiveParameters", "Fixed", "Current"), period = 0, start = NULL, end = NULL )
spec |
the current specification to be refreshed ( |
refspec |
the reference specification used to define the domain
considered for re-estimation ( |
policy |
the refresh policy to apply (see details). |
period , start , end
|
additional parameters used to specify the span on
which additive outliers (AO) are introduced when |
The selection of constraints to be kept fixed or re-estimated is called a revision policy. User-defined parameters are always copied to the new refreshed specifications. In X-13 only the reg-arima part can be refreshed. X-11 decomposition will be completely re-run, keeping all the user-defined parameters from the original specification.
Available refresh policies are:
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)
Fixed: applying the current pre-adjustment reg-arima model and replacing forecasts by new raw data points.
FixedParameters: pre-adjustment reg-arima model is partially modified: regression coefficients will be re-estimated but regression variables, Arima orders and coefficients are unchanged.
FixedAutoRegressiveParameters: same as FixedParameters but Arima Moving Average coefficients (MA) are also re-estimated, Auto-regressive (AR) coefficients are kept fixed.
FreeParameters: all regression and Arima model coefficients are re-estimated, regression variables and Arima orders are kept fixed.
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
Outliers_StochasticComponent: same as "Outliers" but Arima model orders (p,d,q)(P,D,Q) can also be re-identified.
a new specification, an object of class "JD3_X13_SPEC"
or
"JD3_REGARIMA_SPEC"
.
More information on revision policies in JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/t-rev-policies-production
y <- rjd3toolkit::ABS$X0.2.08.10.M # raw series for first estimation y_raw <- window(y, end = c(2016, 12)) # raw series for second (refreshed) estimation y_new <- window(y, end = c(2017, 6)) # specification for first estimation spec_x13_1 <- x13_spec("rsa5c") # first estimation sa_x13 <- x13(y_raw, spec_x13_1) # refreshing the specification current_result_spec <- sa_x13$result_spec current_domain_spec <- sa_x13$estimation_spec # policy = "Fixed" spec_x13_ref <- x13_refresh(current_result_spec, # point spec to be refreshed current_domain_spec, # domain spec (set of constraints) policy = "Fixed" ) # 2nd estimation with refreshed specification sa_x13_ref <- x13(y_new, spec_x13_ref) # policy = "Outliers" spec_x13_ref <- x13_refresh(current_result_spec, current_domain_spec, policy = "Outliers", period = 12, start = c(2017, 1) ) # outliers will be re-detected from January 2017 included # 2nd estimation with refreshed specification sa_x13_ref <- x13(y_new, spec_x13_ref) # policy = "Current" spec_x13_ref <- x13_refresh(current_result_spec, current_domain_spec, policy = "Current", period = 12, start = c(2017, 1), end = end(y_new) ) # points from January 2017 (included) until the end of the series will be treated # as Additive Outliers, the previous reg-Arima model being otherwise kept fixed # 2nd estimation with refreshed specification sa_x13_ref <- x13(y_new, spec_x13_ref)
y <- rjd3toolkit::ABS$X0.2.08.10.M # raw series for first estimation y_raw <- window(y, end = c(2016, 12)) # raw series for second (refreshed) estimation y_new <- window(y, end = c(2017, 6)) # specification for first estimation spec_x13_1 <- x13_spec("rsa5c") # first estimation sa_x13 <- x13(y_raw, spec_x13_1) # refreshing the specification current_result_spec <- sa_x13$result_spec current_domain_spec <- sa_x13$estimation_spec # policy = "Fixed" spec_x13_ref <- x13_refresh(current_result_spec, # point spec to be refreshed current_domain_spec, # domain spec (set of constraints) policy = "Fixed" ) # 2nd estimation with refreshed specification sa_x13_ref <- x13(y_new, spec_x13_ref) # policy = "Outliers" spec_x13_ref <- x13_refresh(current_result_spec, current_domain_spec, policy = "Outliers", period = 12, start = c(2017, 1) ) # outliers will be re-detected from January 2017 included # 2nd estimation with refreshed specification sa_x13_ref <- x13(y_new, spec_x13_ref) # policy = "Current" spec_x13_ref <- x13_refresh(current_result_spec, current_domain_spec, policy = "Current", period = 12, start = c(2017, 1), end = end(y_new) ) # 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 sa_x13_ref <- x13(y_new, spec_x13_ref)
RegARIMA model, pre-adjustment in X13
regarima( ts, spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"), context = NULL, userdefined = NULL ) regarima_fast( ts, spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"), context = NULL, userdefined = NULL )
regarima( ts, spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"), context = NULL, userdefined = NULL ) regarima_fast( ts, spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"), context = NULL, userdefined = NULL )
ts |
an univariate time series. |
spec |
the model specification. Can be either the name of a predefined specification or a user-defined specification. |
context |
list of external regressors (calendar or other) to be used for estimation |
userdefined |
a vector containing additional output variables
(see |
the regarima()
function returns a list with the results
("JD3_REGARIMA_RSLTS"
object), the estimation specification and the result
specification, while regarima_fast()
is a faster function that only returns
the results.
y <- rjd3toolkit::ABS$X0.2.09.10.M sp <- regarima_spec("rg5c") sp <- rjd3toolkit::add_outlier(sp, type = c("AO"), c("2015-01-01", "2010-01-01") ) regarima_fast(y, spec = sp) sp <- rjd3toolkit::set_transform( rjd3toolkit::set_tradingdays( rjd3toolkit::set_easter(sp, enabled = FALSE), option = "workingdays" ), fun = "None" ) regarima_fast(y, spec = sp) sp <- rjd3toolkit::set_outlier(sp, outliers.type = c("AO")) regarima_fast(y, spec = sp)
y <- rjd3toolkit::ABS$X0.2.09.10.M sp <- regarima_spec("rg5c") sp <- rjd3toolkit::add_outlier(sp, type = c("AO"), c("2015-01-01", "2010-01-01") ) regarima_fast(y, spec = sp) sp <- rjd3toolkit::set_transform( rjd3toolkit::set_tradingdays( rjd3toolkit::set_easter(sp, enabled = FALSE), option = "workingdays" ), fun = "None" ) regarima_fast(y, spec = sp) sp <- rjd3toolkit::set_outlier(sp, outliers.type = c("AO")) regarima_fast(y, spec = sp)
Outlier Detection with a RegARIMA Model
regarima_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, clean = FALSE )
regarima_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, clean = FALSE )
y |
the dependent variable (a |
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 |
|
clean |
Clean missing values at the beginning/end of the series. Regression variables are automatically resized, if need be. |
a "JD3_REGARIMA_OUTLIERS"
object, containing input variables and results
regarima_outliers(rjd3toolkit::ABS$X0.2.09.10.M)
regarima_outliers(rjd3toolkit::ABS$X0.2.09.10.M)
Set X-11 Specification
set_x11( x, mode = c(NA, "Undefined", "Additive", "Multiplicative", "LogAdditive", "PseudoAdditive"), seasonal.comp = NA, seasonal.filter = NA, henderson.filter = NA, lsigma = NA, usigma = NA, fcasts = NA, bcasts = NA, calendar.sigma = c(NA, "None", "Signif", "All", "Select"), sigma.vector = NA, exclude.forecast = NA, bias = c(NA, "LEGACY") )
set_x11( x, mode = c(NA, "Undefined", "Additive", "Multiplicative", "LogAdditive", "PseudoAdditive"), seasonal.comp = NA, seasonal.filter = NA, henderson.filter = NA, lsigma = NA, usigma = NA, fcasts = NA, bcasts = NA, calendar.sigma = c(NA, "None", "Signif", "All", "Select"), sigma.vector = NA, exclude.forecast = NA, bias = c(NA, "LEGACY") )
x |
the specification to be modified, object of class "JD3_X11_SPEC", default X11 spec can be obtained as 'x=x11_spec()' |
mode |
character: the decomposition mode. Determines the mode of the
seasonal adjustment decomposition to be performed:
|
seasonal.comp |
logical: if |
seasonal.filter |
a vector of character(s) specifying which seasonal
moving average (i.e. seasonal filter) will be used to estimate the seasonal
factors for the entire series. The vector can be of length: 1 - the same
seasonal filter is used for all periods (e.g.: |
henderson.filter |
numeric: the length of the Henderson filter (odd
number between 3 and 101). If |
lsigma |
numeric: the lower sigma boundary for the detection of extreme values, > 0.5, default=1.5. |
usigma |
numeric: the upper sigma boundary for the detection of extreme values, > lsigma, default=2.5. |
bcasts , fcasts
|
numeric: the number of backasts ( |
calendar.sigma |
character to specify if the standard errors used for
extreme values detection and adjustment are computed: from 5 year spans of
irregulars ( |
sigma.vector |
a vector to specify one of the two groups of periods for
which standard errors used for extreme values detection and adjustment will
be computed separately. Only used if |
exclude.forecast |
Boolean to exclude forecasts and backcasts. If
|
bias |
TODO. |
a "JD3_X11_SPEC" object, containing all the parameters.
x13_spec()
and x11_spec()
.
init_spec <- x11_spec() new_spec <- set_x11(init_spec, mode = "LogAdditive", seasonal.comp = 1, seasonal.filter = "S3X9", henderson.filter = 7, lsigma = 1.7, usigma = 2.7, fcasts = -1, bcasts = -1, calendar.sigma = "All", sigma.vector = NA, exclude.forecast = FALSE, bias = "LEGACY" )
init_spec <- x11_spec() new_spec <- set_x11(init_spec, mode = "LogAdditive", seasonal.comp = 1, seasonal.filter = "S3X9", henderson.filter = 7, lsigma = 1.7, usigma = 2.7, fcasts = -1, bcasts = -1, calendar.sigma = "All", sigma.vector = NA, exclude.forecast = FALSE, bias = "LEGACY" )
Function generating a comprehensive list of available output variables
(series, parameters, diagnostics) from the estimation process by the
x13()
, regarima()
and x11()
functions. Some items are available in the
default estimation output but the remainder can be added using the
userdefined
parameter. User-defined objects can the be retrieved from the
list of lists generated by the estimation process
userdefined_variables_x13(x = c("X-13", "RegArima", "X-11"))
userdefined_variables_x13(x = c("X-13", "RegArima", "X-11"))
x |
a character to indicate the estimation function for which the output items list will be displayed. |
a vector containing the names of all the available output objects (series, diagnostics, parameters)
More information and examples related to 'JDemetra+' features in the online documentation: https://jdemetra-new-documentation.netlify.app/
userdefined_variables_x13("x13") userdefined_variables_x13("regarima") userdefined_variables_x13("x11")
userdefined_variables_x13("x13") userdefined_variables_x13("regarima") userdefined_variables_x13("x11")
X-11 Decomposition Algorithm
x11(ts, spec = x11_spec(), userdefined = NULL)
x11(ts, spec = x11_spec(), userdefined = NULL)
ts |
an univariate time series. |
spec |
the specification. |
userdefined |
a vector containing additional output variables
(see |
y <- rjd3toolkit::ABS$X0.2.09.10.M x11_spec <- x11_spec() x11(y, x11_spec) x11_spec <- set_x11(x11_spec, henderson.filter = 13) x11(y, x11_spec)
y <- rjd3toolkit::ABS$X0.2.09.10.M x11_spec <- x11_spec() x11(y, x11_spec) x11_spec <- set_x11(x11_spec, henderson.filter = 13) x11(y, x11_spec)
Seasonal Adjustment with X13-ARIMA
x13( ts, spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"), context = NULL, userdefined = NULL ) x13_fast( ts, spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"), context = NULL, userdefined = NULL ) .jx13( ts, spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"), context = NULL, userdefined = NULL )
x13( ts, spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"), context = NULL, userdefined = NULL ) x13_fast( ts, spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"), context = NULL, userdefined = NULL ) .jx13( ts, spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"), context = NULL, userdefined = NULL )
ts |
an univariate time series. |
spec |
the model specification. Can be either the name of a predefined specification or a user-defined specification. |
context |
list of external regressors (calendar or other) to be used for estimation |
userdefined |
a vector containing additional output variables
(see |
the x13()
function returns a list with the results, the estimation
specification and the result specification, while x13_fast()
is a faster
function that only returns the results. The .jx13()
functions only returns
results in a java object which will allow to customize outputs in other
packages (use rjd3toolkit::dictionary()
to get the list of variables and
rjd3toolkit::result()
to get a specific variable). In the estimation
functions x13()
and x13_fast()
you can directly use a specification name
(string). If you want to customize a specification you have to create a
specification object first.
y <- rjd3toolkit::ABS$X0.2.09.10.M x13_fast(y, "rsa3") x13(y, "rsa5c") regarima_fast(y, "rg0") regarima(y, "rg3") sp <- x13_spec("rsa5c") sp <- rjd3toolkit::add_outlier(sp, type = c("AO"), c("2015-01-01", "2010-01-01") ) sp <- rjd3toolkit::set_transform( rjd3toolkit::set_tradingdays( rjd3toolkit::set_easter(sp, enabled = FALSE), option = "workingdays" ), fun = "None" ) x13(y, spec = sp) sp <- set_x11(sp, henderson.filter = 13 ) x13_fast(y, spec = sp)
y <- rjd3toolkit::ABS$X0.2.09.10.M x13_fast(y, "rsa3") x13(y, "rsa5c") regarima_fast(y, "rg0") regarima(y, "rg3") sp <- x13_spec("rsa5c") sp <- rjd3toolkit::add_outlier(sp, type = c("AO"), c("2015-01-01", "2010-01-01") ) sp <- rjd3toolkit::set_transform( rjd3toolkit::set_tradingdays( rjd3toolkit::set_easter(sp, enabled = FALSE), option = "workingdays" ), fun = "None" ) x13(y, spec = sp) sp <- set_x11(sp, henderson.filter = 13 ) x13_fast(y, spec = sp)
X-13 Dictionary
x13_dictionary()
x13_dictionary()
A vector containing the names of all the available output objects (series, diagnostics, parameters).
Compute revisions history
x13_revisions( ts, spec, data_ids = NULL, ts_ids = NULL, cmp_ids = NULL, context = NULL )
x13_revisions( ts, spec, data_ids = NULL, ts_ids = NULL, cmp_ids = NULL, context = NULL )
ts |
The time series used for the estimation. |
spec |
The specification used. |
data_ids |
A |
ts_ids |
A |
cmp_ids |
A |
context |
The context of the specification. |
s <- rjd3toolkit::ABS$X0.2.09.10.M sa_mod <- x13(s) data_ids <- list( # Get the coefficient of the trading-day coefficient from 2005-jan list(start = "2005-01-01", id = "regression.td(1)"), # Get the ljung-box statistics on residuals from 2010-jan list(start = "2010-01-01", id = "residuals.lb") ) ts_ids <- list( # Get the SA component estimates of 2010-jan from 2010-jan list(period = "2010-01-01", start = "2010-01-01", id = "sa"), # Get the irregular component estimates of 2010-jan from 2015-jan list(period = "2010-01-01", start = "2015-01-01", id = "i") ) cmp_ids <- list( # Get the SA component estimates (full time series) 2010-jan to 2020-jan list(start = "2010-01-01", end = "2020-01-01", id = "sa"), # Get the trend component estimates (full time series) 2010-jan to 2020-jan list(start = "2010-01-01", end = "2020-01-01", id = "t") ) rh <- x13_revisions(s, sa_mod$result_spec, data_ids, ts_ids, cmp_ids)
s <- rjd3toolkit::ABS$X0.2.09.10.M sa_mod <- x13(s) data_ids <- list( # Get the coefficient of the trading-day coefficient from 2005-jan list(start = "2005-01-01", id = "regression.td(1)"), # Get the ljung-box statistics on residuals from 2010-jan list(start = "2010-01-01", id = "residuals.lb") ) ts_ids <- list( # Get the SA component estimates of 2010-jan from 2010-jan list(period = "2010-01-01", start = "2010-01-01", id = "sa"), # Get the irregular component estimates of 2010-jan from 2015-jan list(period = "2010-01-01", start = "2015-01-01", id = "i") ) cmp_ids <- list( # Get the SA component estimates (full time series) 2010-jan to 2020-jan list(start = "2010-01-01", end = "2020-01-01", id = "sa"), # Get the trend component estimates (full time series) 2010-jan to 2020-jan list(start = "2010-01-01", end = "2020-01-01", id = "t") ) rh <- x13_revisions(s, sa_mod$result_spec, data_ids, ts_ids, cmp_ids)
Set of functions to create default specification objects associated with the X-13ARIMA seasonal adjustment method.
Specification setting of sheer X-11 decomposition method (without reg-arima
pre-adjustment) is supported by the x11_spec()
function only and doesn't
appear among the possible X13-Arima default specifications.
Specification setting can be restricted to the reg-arima part with the
regarima_spec()
function, without argument regarima_spec()
yields a RG5c
specification.
When setting a complete X13-Arima spec, x13_spec()
without argument yields
a RSA5c specification.
regarima_spec(name = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c")) x13_spec(name = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c")) x11_spec()
regarima_spec(name = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c")) x13_spec(name = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c")) x11_spec()
name |
the name of a predefined specification. |
The available predefined 'JDemetra+' model specifications are described in the table below:
Identifier | | Log/level detection | | Outliers detection | | Calendar effects | | ARIMA |
RSA0/RG0 | | NA | | NA | | NA | | Airline(+mean) |
RSA1/RG1 | | automatic | | AO/LS/TC | | NA | | Airline(+mean) |
RSA2c/RG2c | | automatic | | AO/LS/TC | | 2 td vars + Easter | | Airline(+mean) |
RSA3/RG3 | | automatic | | AO/LS/TC | | NA | | automatic |
RSA4c/RG4c | | automatic | | AO/LS/TC | | 2 td vars + Easter | | automatic |
RSA5c/RG5c | | automatic | | AO/LS/TC | | 7 td vars + Easter | | automatic |
an object of class "JD3_X13_SPEC"
(x13_spec()
),
"JD3_REGARIMA_SPEC"
(regarima_spec()
) or
"JD3_X11_SPEC"
(x11_spec()
).
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()
.
To set the decomposition parameters: set_x11()
.
To set the benchmarking parameters: rjd3toolkit::set_benchmarking()
.
init_spec <- x11_spec() init_spec <- regarima_spec("rg4") init_spec <- x13_spec("rsa5c")
init_spec <- x11_spec() init_spec <- regarima_spec("rg4") init_spec <- x13_spec("rsa5c")