Package 'rjd3revisions'

Description

Estimate bias using t-test and augmented t-test

Usage

bias(revisions.view, na.zero = FALSE)

Arguments

See Also

revision_analysis(), render_report()

Examples

## Simulated data
df_long <- simulate_long(
    n_period = 10L * 4L,
    n_revision = 5L,
    periodicity = 4L,
    start_period = as.Date("2010-01-01")
)

## Create vintage and test
vintages <- create_vintages(df_long, periodicity = 4)
revisions <- get_revisions(vintages, gap = 1)
bias(revisions$diagonal_view)

Description

Useful functions to check if a vector represent dates object

Usage

check_date_year(x)

check_date_quarter(x)

check_date_month(x)

check_format_date(x, date_format = "%Y-%m-%d")

Arguments

Details

The function check_date_year checks if the pattern AAAA is recognised. If so, the date will be assimilated with the first January of each year AAAA. The function check_date_quarter checks if the quarterly formats. The accepted formats (for example for the third quarter of 2000) are:

• 2000 T3

• 2000 Q3

• 2000t3

• 2000q3

• 2000T3

• 2000Q3

• 2000 t3

• 2000 q3 If one of the previous formats is recognised, the date will be assimilated with the first day of the quarter of the year (For example 2000 Q3 is assimilated to 2000-07-01). The function check_date_month checks if the monthly formats. The accepted formats (for example for march of 2000) are:

• 2000 M3

• 2000 M03

• 2000 m3

• 2000 m03

• 2000M3

• 2000M03

• 2000m3

• 2000m03 If one of the previous formats is recognised, the date will be assimilated with the first day of the month of the year (For example 2000 M3 is assimilated to 2000-03-01). The function check_format_date checks if the object x match the pattern (or one of the patterns) date_format.

Value

a boolean

Examples

# check_date_year --------------------------------------------------

# Good date (representing years)
check_date_year(x = c("2000", "2001", "2002", "2003"))
check_date_year(x = 2020:2024)

# Bad date
check_date_year(x = "2000 ")
check_date_year(x = 1:4)

# check_date_quarter -----------------------------------------------

# Good date
check_date_quarter(x = c("2000 q2", "2000 q3", "2000 q4", "2001 q1"))
check_date_quarter(x = c("2010T1", "2010T2", "2010T3", "2010T4"))
check_date_quarter(x = c("2020Q1", "2020Q2", "2020Q3", "2020Q4"))
check_date_quarter(x = c("2020Q01", "2020Q02", "2020Q03", "2020Q04"))

# Bad date
check_date_quarter(x = "2000 ")
check_date_quarter(x = 1:4)
check_date_quarter(x = "2000 q 2")
check_date_quarter(x = "2000 q12")

# check_date_month -----------------------------------------------

# Good date (representing years)
check_date_month(x = c("2000 m2", "2000 m3", "2000 m4", "2000 m5"))
check_date_month(x = c("2010M9", "2010M10", "2010M11", "2010M12"))
check_date_month(x = c("2020M111", "2020M12", "2021M01", "2021M02"))
check_date_month(x = c("2020M01", "2020M02", "2020M03", "2020M04"))

# Bad date
check_date_month(x = "2000 ")
check_date_month(x = 1:4)
check_date_month(x = "2000 m 2")
check_date_month(x = "2000 m13")

# check_format_date -----------------------------------------------

# Good date (representing years)
check_format_date(x = c("2000-01-01", "2000-02-01", "2000-03-01", "2000-04-01",
                        "2000-05-01", "2000-06-01", "2000-07-01", "2000-08-01",
                        "2000-09-01", "2000-10-01"),
                 date_format = "%Y-%m-%d")
check_format_date(x = c("01/08/2010", "01/09/2010", "01/10/2010", "01/11/2010",
                        "01/12/2010", "01/01/2011", "01/02/2011", "01/03/2011",
                        "01/04/2011", "01/05/2011"),
                 date_format = "%d/%m/%Y")
check_format_date(x = c("2000-01-01", "2000-02-01", "2000-03-01", "2000-04-01",
                        "2000-05-01", "2000-06-01", "2000-07-01", "2000-08-01",
                        "2000-09-01", "2000-10-01"),
                 date_format = c("%Y-%m-%d", "%d/%m/%Y"))

# Bad date
check_format_date(x = c("2000-01-01", "2000-02-01", "2000-03-01", "2000-04-01",
                        "2000-05-01", "2000-06-01", "2000-07-01", "2000-08-01",
                        "2000-09-01", "2000-10-01"),
                 date_format = "%d/%m/%Y")
check_format_date(x = c("01/08/2010", "01/09/2010", "01/10/2010", "01/11/2010",
                        "01/12/2010", "01/01/2011", "01/02/2011", "01/03/2011",
                        "01/04/2011", "01/05/2011"),
                 date_format = "%Y-%m-%d")

Description

Check horizontal format

Usage

check_horizontal(x, ...)

## S3 method for class 'data.frame'
check_horizontal(x, ...)

## S3 method for class 'matrix'
check_horizontal(x, date_format = "%Y-%m-%d")

## Default S3 method:
check_horizontal(x, ...)

Arguments

Value

the same input but with date formatted

Examples

long_format <- rjd3revisions:::simulate_long(
    start_period = as.Date("2020-01-01"),
    n_period = 24,
    n_revision = 6,
    periodicity = 12L
)
horizontal_format <- rjd3revisions:::from_long_to_horizontal(long_format)
check_horizontal(horizontal_format)

Description

Check long format

Usage

check_long(x, date_format = "%Y-%m-%d")

Arguments

Value

the same input but with column and date formatted

Examples

long_format <- rjd3revisions:::simulate_long(
    start_period = as.Date("2020-01-01"),
    n_period = 24,
    n_revision = 6,
    periodicity = 12L
)
check_long(long_format)

Description

Check vertical format

Usage

check_vertical(x, ...)

## S3 method for class 'mts'
check_vertical(x, periodicity, date_format = "%Y-%m-%d", ...)

## S3 method for class 'data.frame'
check_vertical(x, ...)

## S3 method for class 'matrix'
check_vertical(x, periodicity, date_format = "%Y-%m-%d", ...)

## Default S3 method:
check_vertical(x, ...)

Arguments

Value

the same input but in a ts object and with revision date formatted

Examples

long_format <- rjd3revisions:::simulate_long(
    start_period = as.Date("2020-01-01"),
    n_period = 24,
    n_revision = 6,
    periodicity = 12L
)
vertical_format <- rjd3revisions:::from_long_to_vertical(long_format, periodicity = 12L)
check_vertical(vertical_format)

Description

Cointegration tests (Engle-Granger)

Usage

cointegration(vintages.view, adfk = 1, na.zero = FALSE)

Arguments

See Also

revision_analysis(), render_report()

Examples

## Simulated data
df_long <- simulate_long(
    n_period = 10L * 4L,
    n_revision = 5L,
    periodicity = 4L,
    start_period = as.Date("2010-01-01")
)

## Create vintage and test
vintages <- create_vintages(df_long, periodicity = 4L)
cointegration(vintages$diagonal_view)

Description

Create vintage tables from data.frame, matrix or mts object in R

Usage

create_vintages(x, ...)

## S3 method for class 'data.frame'
create_vintages(
  x,
  type = c("long", "horizontal", "vertical"),
  periodicity,
  date_format = "%Y-%m-%d",
  vintage_selection,
  ...
)

## S3 method for class 'mts'
create_vintages(
  x,
  type = c("long", "horizontal", "vertical"),
  periodicity,
  date_format = "%Y-%m-%d",
  vintage_selection,
  ...
)

## S3 method for class 'matrix'
create_vintages(
  x,
  type = c("long", "horizontal", "vertical"),
  periodicity,
  date_format = "%Y-%m-%d",
  vintage_selection,
  ...
)

## Default S3 method:
create_vintages(x, ...)

Arguments

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.

Value

an object of class rjd3rev_vintages which contains the four different view of a revision

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")
)

Description

Create vintages table from CSV or TXT files

Usage

create_vintages_from_csv(
  file,
  type = c("long", "horizontal", "vertical"),
  periodicity,
  date_format = "%Y-%m-%d",
  ...
)

Arguments

Value

an object of class rjd3rev_vintages

See Also

create_vintages_from_xlsx(), create_vintages() which this function wraps.

Examples

## Not run: 
file_name <- "myinput.csv"
vintages <- create_vintages_from_csv(
    file = file_name,
    type = "vertical",
    periodicity = 12,
    date_format = "%Y-%m-%d",
    sep = ";"
)

## End(Not run)

Description

Create vintages table from XLSX files

Usage

create_vintages_from_xlsx(
  file,
  type = c("long", "horizontal", "vertical"),
  periodicity,
  ...
)

Arguments

Value

an object of class rjd3rev_vintages

See Also

create_vintages_from_csv(), create_vintages() which this function wraps.

Examples

## Not run: 
file_name <- "myinput.xlsx"
sheet_name <- "Sheet1"
vintages <- create_vintages_from_xlsx(
    file = file_name,
    type = "horizontal",
    periodicity = 12L,
    sheet = sheet_name
)

## End(Not run)

Description

Descriptive statistics

Usage

descriptive_statistics(revisions.view, rounding = 3)

Arguments

Examples

## Simulated data
df_long <- simulate_long(
    n_period = 10L * 4L,
    n_revision = 5L,
    periodicity = 4L,
    start_period = as.Date("2010-01-01")
)

## Create vintage and get descriptive statistics of revisions
vintages <- create_vintages(df_long, periodicity = 4)
revisions <- get_revisions(vintages, gap = 1)
descriptive_statistics(revisions$diagonal_view, rounding = 1)

Description

Linear regression model of the revisions (R) on a preliminary vintage (P)

Usage

efficiencyModel1(vintages.view, gap = 1, na.zero = FALSE)

Arguments

See Also

revision_analysis(), render_report()

Examples

## Simulated data
df_long <- simulate_long(
    n_period = 10L * 4L,
    n_revision = 5L,
    periodicity = 4L,
    start_period = as.Date("2010-01-01")
)

## Create vintage and test
vintages <- create_vintages(df_long, periodicity = 4L)
efficiencyModel1(vintages$diagonal_view)

Description

Linear regression model of R_v on R_{v-1}

Usage

efficiencyModel2(vintages.view, gap = 1, na.zero = FALSE)

Arguments

See Also

revision_analysis(), render_report()

Examples

## Simulated data
df_long <- simulate_long(
    n_period = 10L * 4L,
    n_revision = 5L,
    periodicity = 4L,
    start_period = as.Date("2010-01-01")
)

## Create vintage and test
vintages <- create_vintages(df_long, periodicity = 4L)
efficiencyModel2(vintages$diagonal_view)

Description

Calculate revisions from vintages

Usage

get_revisions(vintages, gap = 1)

Arguments

Value

an object of class rjd3rev_revisions which contains the three different views of revisions

See Also

create_vintages()

Examples

df <- data.frame(rev_date = c(rep("2022-07-31",4), rep("2022-08-31",4),
                            rep("2022-09-30",4), rep("2022-10-31",4),
                            rep("2022-11-30",4), rep("2022-12-31",4),
                            rep("2023-01-31",4), rep("2023-02-28",4)),
                 time_period = c(rep(c("2022Q1","2022Q2","2022Q3","2022Q4"),8)),
                 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 <- create_vintages(df, periodicity = 4)
revisions <- get_revisions(vintages, gap = 1)

Description

Linear regression model of R_v on R_{v-1},...,R_{v-p}. (p=nrevs)

Usage

orthogonallyModel1(revisions.view, nrevs = 1, na.zero = FALSE)

Arguments

See Also

revision_analysis(), render_report()

Examples

## Simulated data
df_long <- simulate_long(
    n_period = 10L * 4L,
    n_revision = 5L,
    periodicity = 4L,
    start_period = as.Date("2010-01-01")
)

## Create vintage and test
vintages <- create_vintages(df_long, periodicity = 4L)
revisions <- get_revisions(vintages, gap = 1)
orthogonallyModel1(revisions$diagonal_view)

Description

Linear regression model of R_v on R_{v-k} (k = reference)

Usage

orthogonallyModel2(revisions.view, reference = 1, na.zero = FALSE)

Arguments

See Also

revision_analysis(), render_report()

Examples

## Simulated data
df_long <- simulate_long(
    n_period = 10L * 4L,
    n_revision = 5L,
    periodicity = 4L,
    start_period = as.Date("2010-01-01")
)

## Create vintage and test
vintages <- create_vintages(df_long, periodicity = 4L)
revisions <- get_revisions(vintages, gap = 1)
orthogonallyModel2(revisions$diagonal_view)

Description

Plot function for objects of class "rjd3rev_revisions"

Usage

## S3 method for class 'rjd3rev_revisions'
plot(x, view = c("vertical", "diagonal"), n_rev = 2, ...)

Arguments

Description

Plot function for objects of class "rjd3rev_vintages"

Usage

## S3 method for class 'rjd3rev_vintages'
plot(x, col, ...)

Arguments

Description

Print function for objects of class "rjd3rev_revisions"

Usage

## S3 method for class 'rjd3rev_revisions'
print(x, n_row = 12, n_col = 3, ...)

Arguments

Description

Print function for objects of class rjd3rev_rslts

Usage

## S3 method for class 'rjd3rev_rslts'
print(x, ...)

Arguments

Description

Print function for objects of class "rjd3rev_vintages"

Usage

## S3 method for class 'rjd3rev_vintages'
print(x, n_row = 8, n_col = 3, ...)

Arguments

Description

Generate report on Revision Analysis

Usage

render_report(
  rslt,
  output_file,
  output_dir,
  output_format = c("html_document", "pdf_document", "word_document"),
  plot_revisions = FALSE,
  open_report = TRUE,
  ...
)

Arguments

See Also

revision_analysis() to create the input object

Examples

## Simulated data
df_long <- simulate_long(
    n_period = 10L * 4L,
    n_revision = 5L,
    periodicity = 4L,
    start_period = as.Date("2010-01-01")
)

## Make analysis and generate the report

vintages <- create_vintages(df_long, periodicity = 4L, type = "long")
rslt <- revision_analysis(vintages, view = "diagonal")

## Not run: 
render_report(
    rslt,
    output_file = "my_report",
    output_dir = "C:/Users/xxx",
    output_format = "pdf_document",
    plot_revisions = TRUE
)

## End(Not run)

Description

The function perform parametric tests which enable the users to detect potential bias (both mean and regression bias) and sources of inefficiency in preliminary estimates. We would conclude to inefficiency in the preliminary estimates when revisions are predictable in some way. In the results, parametric tests are divided into 5 categories: relevancy (check whether preliminary estimates are even worth it), bias, efficiency, orthogonality (correlation at higher lags), and signalVSnoise. Descriptive statistics on revisions are also provided. For some of the parametric tests, prior transformation of the vintage data may be important to avoid misleading results. By default, the decision to differentiate the vintage data is performed automatically based on unit root and co-integration tests whose results can be found found in the results too (section 'varbased'). Finally, running the function render_report() on the output of revision_analysis() would give you both a formatted summary of the results and full explanations about each tests.

Usage

revision_analysis(
  vintages,
  gap = 1,
  view = c("vertical", "diagonal"),
  n.releases = 3,
  transf.diff = c("auto", "forced", "none"),
  transf.log = FALSE,
  descriptive.rounding = 3,
  nrevs = 1,
  ref = 1,
  na.zero = FALSE
)

Arguments

Value

an object of class 'rjd3rev_rslts'

See Also

create_vintages() to create the input object, render_report() to get a summary and information the tests

Examples

## Simulated data

df_long <- simulate_long(
    n_period = 10L * 4L,
    n_revision = 10L,
    periodicity = 4L,
    start_period = as.Date("2010-01-01")
)

## Create a `"rjd3rev_vintages"` object with the input
vintages <- create_vintages(x = df_long, periodicity = 4L, date_format = "%Y-%m-%d")
# revisions <- get_revisions(vintages, gap = 1L) # just to get a first insight of the revisions

## Call using all default parameters
rslt1 <- revision_analysis(vintages)
# render_report(rslt1, output_file = "report1", output_dir = "C:/Users/xxx")
summary(rslt1) # formatted summary only
View(rslt1) # formatted tables in viewer panel

## Calls using diagonal view (suited in many situations such as to evaluate GDP estimates)
## Note: when input are not growth rates but the gross series, differentiation is
## performed automatically (if transf.diff is let to its default option) but `transf.log`
## must be set to TRUE manually whenever a log-transformation of the data is necessary
rslt2 <- revision_analysis(vintages, gap = 1, view = "diagonal", n.releases = 3)
# render_report(rslt2, output_file = "report2", output_dir = "C:/Users/xxx",
#               output_format = "word_document", plot_revisions = TRUE)
summary(rslt2)
View(rslt2)

## Call to evaluate revisions for a specific range of vintage periods
vintages <- create_vintages(
    x = df_long,
    periodicity = 4L,
    vintage_selection = c(start = "2012-12-31", end = "2018-06-30")
)
rslt3 <- revision_analysis(vintages, gap = 2, view = "vertical")
#render_report(rslt3, output_file = "report2", output_dir = "C:/Users/xxx", plot_revisions = TRUE)
summary(rslt3)
View(rslt3)

## Note that it is possible to change thresholds values for quality
## assessment using options (see vignette for details)
options(
    augmented_t_threshold = c(severe = 0.005, bad = 0.01, uncertain = 0.05),
    slope_and_drift_threshold = c(severe = 0.005, bad = 0.05, uncertain = 0.10),
    theil_u2_threshold = c(uncertain = .5, bad = .7, severe = 1)
)
rslt4 <- revision_analysis(vintages, gap = 1, view = "diagonal", n.releases = 3)
summary(rslt4)
View(rslt4)

Description

Set all test thresholds to their default values

Usage

set_all_thresholds_to_default(diagnostic_tests = TRUE)

Arguments

Examples

set_all_thresholds_to_default()

Description

Set thresholds of a given test to their default values

Usage

set_thresholds_to_default(threshold_option_name)

Arguments

Examples

set_thresholds_to_default("t_threshold")

Description

Linear regression models to determine whether revisions are ‘news’ or ‘noise’. For 'noise': R (revisions) on P (preliminary estimate). For 'news': R on L (latter estimate).

Usage

signalnoise(vintages.view, gap = 1, na.zero = FALSE)

Arguments

See Also

revision_analysis(), render_report()

Examples

## Simulated data
df_long <- simulate_long(
    n_period = 10L * 4L,
    n_revision = 5L,
    periodicity = 4L,
    start_period = as.Date("2010-01-01")
)

## Create vintage and test
vintages <- create_vintages(df_long, periodicity = 4L)
signalnoise(vintages$diagonal_view)

Description

Simulate long datasets with revisions

Usage

simulate_long(
  n_period = 50,
  n_revision = 10,
  start_period = as.Date("2012-01-01"),
  periodicity = 12L
)

Arguments

Value

A dataset in the long format. See create_vintages for more information about the different data formats.

Examples

simulate_long(n_period = 100L, n_revision = 10L)
simulate_long(periodicity = 1L)
simulate_long(start_period = as.Date("2000-01-01"),
              n_period = 10L * 12L,
              periodicity = 12L)
simulate_long(periodicity = 4L, n_period = 5L * 4L)

Description

Linear regression model of a latter vintage (L) on a preliminary vintage (P)

Usage

slope_and_drift(vintages.view, gap = 1, na.zero = FALSE)

Arguments

See Also

revision_analysis(), render_report()

Examples

## Simulated data
df_long <- simulate_long(
    n_period = 10L * 4L,
    n_revision = 5L,
    periodicity = 4L,
    start_period = as.Date("2010-01-01")
)

## Create vintage and test
vintages <- create_vintages(df_long, periodicity = 4L)
slope_and_drift(vintages$diagonal_view)

Description

Summary function for objects of class "rjd3rev_revisions"

Usage

## S3 method for class 'rjd3rev_revisions'
summary(object, ...)

Arguments

Description

Summary function for objects of class rjd3rev_rslts

Usage

## S3 method for class 'rjd3rev_rslts'
summary(object, ...)

Arguments

Description

Summary function for objects of class "rjd3rev_vintages"

Usage

## S3 method for class 'rjd3rev_vintages'
summary(object, ...)

Arguments

Description

Theil's Inequality Coefficient U1

Usage

theil(vintages.view, gap = 1, na.zero = FALSE)

Arguments

See Also

revision_analysis(), render_report()

Examples

## Simulated data
df_long <- simulate_long(
    n_period = 10L * 4L,
    n_revision = 5L,
    periodicity = 4L,
    start_period = as.Date("2010-01-01")
)

## Create vintage and test
vintages <- create_vintages(df_long, periodicity = 4)
theil(vintages$diagonal_view)

Description

Theil's Inequality Coefficient U2

Usage

theil2(vintages.view, gap = 1, na.zero = FALSE)

Arguments

See Also

revision_analysis(), render_report()

Examples

## Simulated data
df_long <- simulate_long(
    n_period = 10L * 4L,
    n_revision = 5L,
    periodicity = 4L,
    start_period = as.Date("2010-01-01")
)

## Create vintage and test
vintages <- create_vintages(df_long, periodicity = 4)
theil2(vintages$diagonal_view)

Description

Unit root test

Usage

unitroot(vintages.view, adfk = 1, na.zero = FALSE)

Arguments

See Also

revision_analysis(), render_report()

Examples

## Simulated data
df_long <- simulate_long(
    n_period = 10L * 4L,
    n_revision = 5L,
    periodicity = 4L,
    start_period = as.Date("2010-01-01")
)

## Create vintage and test
vintages <- create_vintages(df_long, periodicity = 4L)
unitroot(vintages$diagonal_view)

Description

Can lead to a better understanding of the nature of any nonstationary process among the different component series.

Usage

vecm(
  vintages.view,
  lag = 2,
  model = c("none", "cnt", "trend"),
  na.zero = FALSE
)

Arguments

Examples

## Simulated data
df_long <- simulate_long(
    n_period = 10L * 4L,
    n_revision = 5L,
    periodicity = 4L,
    start_period = as.Date("2010-01-01")
)

## Create vintage and test
vintages <- create_vintages(df_long, periodicity = 4L)
vecm(vintages$diagonal_view)

Description

Display the different view in a different panel to visualize the data in a table / matrix format

Usage

View(x, ...)

## Default S3 method:
View(x, ...)

## S3 method for class 'rjd3rev_vintages'
View(x, type = c("all", "long", "horizontal", "vertical", "diagonal"), ...)

Arguments

Details

Generate the view of the vintages in different format. With the type argument, you can choose the view to display. You can choose between the long, horizontal, vertical and diagonal view.

Description

View function for objects of class rjd3rev_rslts

Usage

## S3 method for class 'rjd3rev_rslts'
View(x, type = c("summary", "stats-desc", "revisions", "tests"), ...)

Arguments