chaining {hicp}

Chain-linking, rebasing and frequency conversion

Description

Function unchain() decouples a chained index series with monthly frequency. These unchained index series can be aggregated into higher-level indices using aggregate(). To obtain a longterm index series, the higher-level indices must be chained using function chain(). Finally, rebase() sets the index reference period. Monthly indices can be converted into annual or quarterly indices using function convert().

Usage

unchain(x, t, by=12)

chain(x, t, by=12)

rebase(x, t, t.ref, verbose=FALSE)

convert(x, t, freq="annual")

Arguments

x

numeric vector of index values

t

date vector

by

for annual overlap NULL; for one-month overlap a single integer between 1 and 12 specifying the price reference month

t.ref

character specifying the index reference period. Could be a whole year (YYYY) or a single year-month (YYYY-MM).

verbose

logical indicating if messages regarding the index reference period should be printed to the console or not.

freq

frequency of converted index. Either annual or quarterly.

Details

Function unchain() sets the value of the first price reference period to NA although the value could be set to 100 (if by is not NULL) or 100 divided by the average of the year (if by=NULL). This is wanted to avoid aggregation of these values. Function chain() finally sets the values back to 100.

Value

Functions unchain(), chain() and rebase() return numeric values of the same length as x.

Function convert() returns a named vector of the length of quarter or years available in t, where the names correspond to the years or quarters.

Author(s)

Sebastian Weinand

References

European Commission, Eurostat, Harmonised Index of Consumer Prices (HICP) - Methodological Manual - 2024 edition, Publications Office of the European Union, 2024, https://data.europa.eu/doi/10.2785/055028.

See Also

aggregate

Examples

### EXAMPLE 1

t <- seq.Date(from=as.Date("2021-12-01"), to=as.Date("2024-12-01"), by="1 month")
p <- rnorm(n=length(t), mean=100, sd=5)

100*p/p[1]
chain(unchain(p, t, by=12), t, by=12)

convert(x=p, t=t, freq="q") # quarterly index

t <- seq.Date(from=as.Date("2021-01-01"), to=as.Date("2024-12-01"), by="1 month")
p <- rnorm(n=length(t), mean=100, sd=5)

100*p/mean(p[1:12])
(res <- chain(unchain(p, t, by=NULL), t, by=NULL))
# note that for backwards compability, each month in the first
# year receives an index value of 100. this allows the same
# computation again:
chain(unchain(res, t, by=NULL), t, by=NULL)

### EXAMPLE 2

# set cores for testing on CRAN:
library(restatapi)
options(restatapi_cores=1)
library(data.table)

# get hicp index values for euro area with base 2015:
dt <- hicp.dataimport(id="prc_hicp_midx", filter=list(unit="I15", geo="EA"))
dt[, "time":=as.Date(paste0(time, "-01"))]
setkeyv(x=dt, cols=c("unit","coicop","time"))

# check chain-linked indices against published data:
dt[, "dec_ratio" := unchain(x=values, t=time), by="coicop"]
dt[, "chained_index" := chain(x=dec_ratio, t=time), by="coicop"]
dt[, "index_own" := rebase(x=chained_index, t=time, t.ref="2015"), by="coicop"]
dt[abs(values-index_own)>0.01,] # should be empty

# check converted indices against published data:
dta <- dt[, as.data.table(convert(x=values, t=time), keep.rownames=TRUE), by="coicop"]
setnames(x=dta, c("coicop","time","index"))
aind <- hicp.dataimport(id="prc_hicp_aind", filter=list(unit="INX_A_AVG", geo="EA"))
aind[, c("geo","unit") := NULL]
dtcomp <- merge(x=aind, y=dta, by=c("coicop","time"), all=TRUE)
dtcomp[abs(values-index)>0.01,] # should be empty