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{rjd3x13} offers full access to options and outputs of X-13
(rjd3x13::x13()), including RegARIMA modelling
(rjd3x13::regarima()) and X-11 decomposition
(rjd3x13::x11()).
A specification can be created with
rjd3x13::regarima_spec(), rjd3x13::x11_spec()
or rjd3x13::x13_spec() and can be modified with the
following functions:
for pre-processing: 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();
for decomposition: rjd3x13::set_x11();
for benchmarking:
rjd3toolkit::set_benchmarking().
Running rjd3 packages requires Java 21 or higher. How to set up such a configuration in R is explained here
🎉 {rjd3x13} is now available on CRAN! 🎉
To install it, you have to launch the following command line:
install.packages("rjd3x13")To get the current development version of {rjd3x13} from GitHub with:
# install.packages("remotes")
remotes::install_github("rjdverse/rjd3x13")library("rjd3x13")
y <- rjd3toolkit::ABS$X0.2.09.10.M
x13_model <- x13(y)
summary(x13_model$result$preprocessing) # Summary of regarima model
#> Log-transformation: yes
#> SARIMA model: (2,1,1) (0,1,1)
#>
#> Coefficients
#> Estimate Std. Error T-stat Pr(>|t|)
#> phi(1) 0.34740 0.06502 5.343 1.53e-07 ***
#> phi(2) 0.21733 0.06000 3.622 0.000329 ***
#> theta(1) -0.69937 0.05115 -13.672 < 2e-16 ***
#> btheta(1) -0.48038 0.06993 -6.869 2.45e-11 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Regression model:
#> Estimate Std. Error T-stat Pr(>|t|)
#> td 0.0023233 0.0006844 3.395 0.000755 ***
#> easter 0.0520113 0.0084894 6.127 2.14e-09 ***
#> TC (2000-06-01) 0.1590340 0.0288578 5.511 6.38e-08 ***
#> AO (2000-07-01) -0.2900774 0.0400551 -7.242 2.26e-12 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> Number of observations: 425, Number of effective observations: 412, Number of parameters: 9
#> Loglikelihood: 746.7517, Adjusted loglikelihood: -2120.875
#> Standard error of the regression (ML estimate): 0.03927991
#> AIC: 4259.75, AICc: 4260.198, BIC: 4295.939
plot(x13_model) # Plot of the final decomposition
Any contribution is welcome and should be done through pull requests and/or issues. pull requests should include updated tests and updated documentation. If functionality is changed, docstrings should be added or updated.
The code of this project is licensed under the European Union Public Licence (EUPL).
These 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.
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