rjd3revisions

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.

Installation

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):

# install.packages("remotes")
remotes::install_github("rjdverse/rjd3toolkit@*release")
remotes::install_github("rjdverse/rjd3revisions@*release", build_vignettes = TRUE)

To get the current development version from GitHub:

# install.packages("remotes")
remotes::install_github("rjdverse/rjd3revisions", build_vignettes = TRUE)

Usage

First 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.

Format 1: long view

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
…	…	…

Format 2: vertical view

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

Format 3: horizontal view

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.

# Examples

# Long format
long_view <- data.frame(
    rev_date = rep(x = c("2022-07-31", "2022-08-31", "2022-09-30", "2022-10-31",
                         "2022-11-30", "2022-12-31", "2023-01-31", "2023-02-28"),
                   each = 4L),
    time_period = rep(x = c("2022Q1", "2022Q2", "2022Q3", "2022Q4"), times = 8L),
    obs_values = c(
        .8, .2, NA, NA, .8, .1, NA, NA,
        .7, .1, NA, NA, .7, .2, .5, NA,
        .7, .2, .5, NA, .7, .3, .7, NA,
        .7, .2, .7, .4, .7, .3, .7, .3
    )
)

# Horizontal format
horizontal_view <- matrix(data = c(.8, .8, .7, .7, .7, .7, .7, .7, .2, .1,
                            .1, .2, .2, .3, .2, .3, NA, NA, NA, .5, .5, .7, .7,
                            .7, NA, NA, NA, NA, NA, NA, .4, .3),
                          ncol = 4)
colnames(horizontal_view) <- c("2022Q1", "2022Q2", "2022Q3", "2022Q4")
rownames(horizontal_view) <- c("2022-07-31", "2022-08-31", "2022-09-30", "2022-10-31",
                               "2022-11-30", "2022-12-31", "2023-01-31", "2023-02-28")

# Vertical format
vertical_view <- matrix(data = c(.8, .2, NA, NA, .8, .1, NA, NA, .7, .1, NA,
                                 NA, .7, .2, .5, NA, .7, .2, .5, NA, .7, .3, .7, NA,
                                 .7, .2, .7, .4, .7, .3, .7, .3),
                          nrow = 4)
rownames(vertical_view) <- c("2022Q1", "2022Q2", "2022Q3", "2022Q4")
colnames(vertical_view) <- c("2022-07-31", "2022-08-31", "2022-09-30", "2022-10-31",
                               "2022-11-30", "2022-12-31", "2023-01-31", "2023-02-28")

Then you can create your vintages, inspect revisions (optional) and make the analysis

library("rjd3revisions")

vintages <- create_vintages(df, periodicity = 4)
# revisions <- get_revisions(vintages, gap = 2)
# plot(revisions)
rslt <- revision_analysis(vintages, gap = 1, view = "diagonal", n.releases = 3)

Finally to create a report and get a summary of the results, you can use

render_report(
    rslt,
    output_file = "my_report",
    output_dir = "C:/Users/xxx",
    output_format = "pdf_document"
)

summary(rslt)
print(rslt)

Additional information

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.

Package Maintenance and contributing

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.

Licensing

The code of this project is licensed under the European Union Public Licence (EUPL).