The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
rjd3bridge is an R package designed to bridge the gap
between RJDemetra (based on JDemetra+ version 2) and
the modern rjd3 package suite
(rjd3x13 and rjd3tramoseats, based on
JDemetra+ version 3).
It provides bidirectional conversion tools (as_rjd2()
and as_rjd3() functions) to seamlessly translate seasonal
adjustment specifications and fitted model objects across both ecosystem
versions without needing to rewrite custom configurations from
scratch.
To install the developpment version:
install.packages('rjd3bridge', repos = c('https://aqlt.r-universe.dev', 'https://cloud.r-project.org'))# install.packages("remotes")
remotes::install_github("AQLT/rjd3bridge")library(rjd3bridge)
y <- RJDemetra::ipi_c_eu[, "FR"]
model_v2 <- RJDemetra::x13(y, spec = "RSA5c")
model_v3 <- as_rjd3(model_v2)
# Convert only a specification object
spec_v2 <- RJDemetra::x13_spec("RSA5c")
spec_v3 <- as_rjd3(spec_v2)library(rjd3bridge)
y <- rjd3toolkit::ABS$X0.2.09.10.M
model_v3 <- rjd3x13::x13(y)
model_v2 <- as_rjd2(model_v3)The modelling_context has to be provided if the
specification contains user-defined variables. Otherwise, the conversion
will be complete:
library(rjd3bridge)
y <- rjd3toolkit::ABS$X0.2.09.10.M
regs_td <- rjd3toolkit::td(
s = y,
groups = c(1, 2, 0, 4, 5, 6, 3),
contrasts = TRUE
)
colnames(regs_td) <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")
context <- rjd3toolkit::modelling_context(variables = list(regs_td))
spec <- rjd3tramoseats::tramoseats_spec() |>
rjd3toolkit::set_tradingdays(
option = "UserDefined",
uservariable = c("r.Monday", "r.Tuesday", "r.Wednesday",
"r.Thursday", "r.Friday", "r.Saturday"),
test = "None"
)
model_v3 <- rjd3tramoseats::tramoseats(y, spec, context)
model_v2 <- as_rjd2(model_v3, context = context)For preprocessing models (RegARIMA or TRAMO), the input time series
y must be provided to the conversion function. Otherwise
the start date of the time series will default to year 1, which may lead
to incorrect results (e.g., for outliers).
model_v3 <- rjd3tramoseats::tramo(y)
as_rjd2(model_v3)
#> y = regression model + arima (0, 1, 2, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Theta(1) -1.1304 0.047
#> Theta(2) 0.3088 0.047
#> BTheta(1) -0.6523 0.037
#>
#> Estimate Std. Error
#> Mean -3.044e-05 0
#> Week days -6.643e-04 0
#>
#>
#> Residual standard error: 0.00614 on 406 degrees of freedom
#> Log likelihood = 1510, aic = -1410 aicc = -1409, bic(corrected for length) = -10.11
as_rjd2(model_v3, y = y)
#> y = regression model + arima (0, 1, 1, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Theta(1) -0.8278 0.028
#> BTheta(1) -0.4255 0.045
#>
#> Estimate Std. Error
#> Monday -0.0109445 0.003
#> Tuesday 0.0048940 0.004
#> Wednesday 0.0001761 0.003
#> Thursday 0.0132928 0.004
#> Friday -0.0024801 0.004
#> Saturday 0.0153509 0.004
#> Leap year 0.0410667 0.010
#> Easter [6] 0.0503888 0.007
#> AO (7-2000) -0.1972355 0.030
#> AO (6-2000) 0.1681679 0.030
#>
#>
#> Residual standard error: 0.03616 on 399 degrees of freedom
#> Log likelihood = 781.4, aic = 4199 aicc = 4199, bic(corrected for length) = -6.464These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.