Create vintage tables from data.frame, matrix or mts object in R
Usage
create_vintages(x, ...)
# S3 method for data.frame
create_vintages(
x,
type = c("long", "horizontal", "vertical"),
periodicity,
date_format = "%Y-%m-%d",
vintage_selection,
...
)
# S3 method for mts
create_vintages(
x,
type = c("long", "horizontal", "vertical"),
periodicity,
date_format = "%Y-%m-%d",
vintage_selection,
...
)
# S3 method for matrix
create_vintages(
x,
type = c("long", "horizontal", "vertical"),
periodicity,
date_format = "%Y-%m-%d",
vintage_selection,
...
)
# S3 method for default
create_vintages(x, ...)Arguments
- x
a formatted object containing the input. It can be of type `data.frame`, `matrix` or `mts` and must represent one of the multiple vintage views (selected by the argument `type`.
- ...
Arguments to be passed to `create_vintages` according to the class of the object `x`
- type
character specifying the type of representation of the input between `"long"`, `"horizontal"` and `"vertical"` approach.
- periodicity
periodicity of the time period (12, 4 or 1 for resp. monthly, quarterly or annual data)
- date_format
characterstring corresponding to the format used in the input data.frame for the revision dates.- vintage_selection
Datevector (or a character vector with the same format as date_format) of length <= 2, specifying the range of revision dates to retain. As an example: c(start = "2022-02-02", end = "2022-08-05") or c(start = as.Date("2022-02-02"), end = as.Date("2022-08-05")) would keep all the vintages whose revision date is between 02 Feb. 2022 and 05 Aug. 2022. If missing (by default), the whole range is selected.
Details
From the input data.frame, the function displays vintages considering three different data structures or views: vertical, horizontal and diagonal. See the `details` section below for more information on the different views. The function returns an object of class `rjd3rev_vintages` that can be used as input in the main function `revision_analysis`.
The are four different vintage views:
1. The vertical view shows the observed values at each time period by the different vintages. This approach is robust to changes of base year and data redefinition. A drawback of this approach is that for comparing the same historical series for different vintages, we need to look at the smallest common number of observations and consequently the number of observations is in some circumstances very small. Moreover, it is often the the case that most of the revision is about the last few points of the series so that the number of observations is too small to test anything.
2. The horizontal view shows the observed values of the different vintages by the period. A quick analysis can be performed by rows in order to see how for the same data point (e.g. 2023Q1), figures are first estimated, then forecasted and finally revised. The main findings are usually obvious: in most cases the variance decreases, namely data converge towards the 'true value'. Horizontal tables are just a transpose of vertical tables and are not used in the tests in `revision_analysis`.
3. The diagonal view shows subsequent releases of a given time period, without regard for the date of publication. The advantage of the diagonal approach is that it gives a way to analyse the trade between the timing of the release and the accuracy of the published figures. It is particularly informative when regular estimation intervals exist for the data under study. However, this approach requires to be particularly vigilant in case there is a change in base year or data redefinition.
4. The long view is a representation of data that allows information to be grouped together in order to facilitate their manipulation. With 3 columns (1 column for the time period, 1 column for the publication / revision date and one column for the data), this representation allows for efficient and non-redundant storage of data.
Examples
## creating the input
# 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
)
)
vintages_1 <- create_vintages(x = long_view, type = "long", periodicity = 4)
# 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")
vintages_2 <- create_vintages(x = horizontal_view, type = "horizontal", periodicity = 4)
# Horizontal 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")
vintages_3 <- create_vintages(x = vertical_view, type = "vertical", periodicity = 4)
## specifying the format of revision dates
vintages <- create_vintages(
x = long_view,
type ="long",
periodicity = 4L,
date_format= "%Y-%m-%d"
)
## including vintage selection
vintages <- create_vintages(
x = long_view,
type ="long",
periodicity = 4L,
date_format= "%Y-%m-%d",
vintage_selection = c(start="2022-10-31", end="2023-01-31")
)