Most economic indicators are published several times for a given reporting period, this entails revisions as time horizon changes. rjd3revisions performs a battery of tests on these revisions and generates a report with the results. The various tests enable the users to detect potential bias and sources of inefficiency in preliminary estimates.
Running rjd3 packages requires Java 17 or higher. How to set up such a configuration in R is explained here
To get the current stable version (from the latest release):
- From GitHub:
# install.packages("remotes")
remotes::install_github("rjdverse/rjd3toolkit@*release")
remotes::install_github("rjdverse/rjd3revisions@*release", build_vignettes = TRUE)- From r-universe:
install.packages("rjd3revisions", repos = c("https://rjdverse.r-universe.dev", "https://cloud.r-project.org"))You can install the development version of rjd3revisions from GitHub with:
# install.packages("remotes")
remotes::install_github("rjdverse/rjd3revisions")library("rjd3revisions")
#>
#> Attaching package: 'rjd3revisions'
#> The following object is masked from 'package:utils':
#>
#> ViewFirst you need to get your input data set as a data.frame in R in a specific format as below. Note that missing values can either be mentioned as NA (as in the example below) or not be included in the input at the best convenience of the user.
| rev_date | time_period | obs_values |
|---|---|---|
| 2022-07-31 | 2022Q1 | 0.8 |
| 2022-07-31 | 2022Q2 | 0.2 |
| 2022-07-31 | 2022Q3 | NA |
| 2022-07-31 | 2022Q4 | NA |
| 2022-08-31 | 2022Q1 | 0.8 |
| … | … | … |
| Period | 2023/03/31 | 2023/04/30 | 2023/05/31 |
|---|---|---|---|
| 2022M01 | 15.2 | 15.1 | 15.0 |
| 2022M02 | 15.0 | 14.9 | 14.9 |
| … | … | … | … |
| 2023M01 | 13.0 | 13.1 | 13.2 |
| 2023M02 | 12.1 | 12.1 | |
| 2023M03 | 12.3 |
| Period | 2022M01 | 2022M02 | … | 2023M01 | 2023M02 | 2023M03 |
|---|---|---|---|---|---|---|
| 2023/03/31 | 15.2 | 15.0 | … | 13.0 | ||
| 2023/04/30 | 15.1 | 14.9 | … | 13.1 | 12.1 | |
| 2023/05/31 | 15.0 | 14.9 | … | 13.2 | 12.1 | 12.3 |
Depending on the location of your input data, you can use
create_vintages_from_xlsx() or create_vintages_from_csv(), or the
more generic function create_vintages() to create the vintages.
set.seed(7)
# Examples
long_view <- simulate_long(
periodicity = 4,
n_period = 4 * 10,
n_revision = 7,
start_period = as.Date("2000-01-01")
)Then you can create your vintages and plot the vintages and inspect the revisions (optional)
vintages <- create_vintages(long_view, periodicity = 4)
plot(vintages, lwd = 2)
revisions <- get_revisions(vintages, gap = 2)
plot(revisions)
and make the analysis
rslt <- revision_analysis(vintages, gap = 1, view = "diagonal", n.releases = 3)
print(rslt)
#> Transf. [Release[2]]-[Release[1]]
#> Relevancy - Theil U2 None Good (0.339)
#> Bias1 T-test None Good (0.798)
#> Bias2 Augmented T-test None Good (0.823)
#> Bias3 SlopeAndDrift (Ols L on P) - Mean None Good (0.347)
#> Bias3 SlopeAndDrift (Ols L on P) - Reg. None Good (0.232)
#> Efficiency1 (Ols R on P) - Mean Delta 1 Good (0.166)
#> Efficiency1 (Ols R on P) - Reg. Delta 1 Good (0.107)
#> Efficiency2 (Ols Rv on Rv_1) - Mean None
#> Efficiency2 (Ols Rv on Rv_1) - Reg. None
#> Orthogonality1 (Ols Rv on Rv_(1:p)) - Mean None
#> Orthogonality1 (Ols Rv on Rv_(1:p)) - Reg. None
#> Orthogonality2 (Ols Rv on Rv_k.) - Mean None
#> Orthogonality2 (Ols Rv on Rv_k) - Reg. None
#> Orthogonality3 AutoCorrelation (Ljung-Box) None Good (0.417)
#> Orthogonality4 Seasonality (Ljung-Box) Delta 1 Good (0.993)
#> Orthogonality4 Seasonality (Friedman) Delta 1 Good (0.137)
#> SignalVsNoise1 - Noise (Ols R on P) Delta 1 Good (0.082)
#> SignalVsNoise2 - Signal (Ols R on L) Delta 1 Uncertain (0.053)
#> [Release[3]]-[Release[2]]
#> Relevancy - Theil U2 Good (0.225)
#> Bias1 T-test Good (0.805)
#> Bias2 Augmented T-test Good (0.806)
#> Bias3 SlopeAndDrift (Ols L on P) - Mean Good (0.584)
#> Bias3 SlopeAndDrift (Ols L on P) - Reg. Good (0.624)
#> Efficiency1 (Ols R on P) - Mean Good (0.300)
#> Efficiency1 (Ols R on P) - Reg. Good (0.240)
#> Efficiency2 (Ols Rv on Rv_1) - Mean Good (0.818)
#> Efficiency2 (Ols Rv on Rv_1) - Reg. Good (0.298)
#> Orthogonality1 (Ols Rv on Rv_(1:p)) - Mean Good (0.818)
#> Orthogonality1 (Ols Rv on Rv_(1:p)) - Reg. Good (0.702)
#> Orthogonality2 (Ols Rv on Rv_k.) - Mean Good (0.818)
#> Orthogonality2 (Ols Rv on Rv_k) - Reg. Good (0.298)
#> Orthogonality3 AutoCorrelation (Ljung-Box) Good (0.999)
#> Orthogonality4 Seasonality (Ljung-Box) Good (1.000)
#> Orthogonality4 Seasonality (Friedman) Good (0.769)
#> SignalVsNoise1 - Noise (Ols R on P) Good (0.248)
#> SignalVsNoise2 - Signal (Ols R on L) Uncertain (0.856)
# summary(rslt)
View(rslt)
Finally to create a report and get a summary of the results, you can use
render_report(
rslt,
output_file = "my_report",
output_dir = tempdir(),
output_format = "pdf_document"
)This README.md file gives you a quick example of how to proceed. Consult
the vignette (with browseVignettes("rjd3revisions")) and the
documentation of each function (for example: ?create_vintages,
?revision_analysis) to see the current possibilities of the tool.
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).