| index.aggregation {hicp} | R Documentation |
Index number functions and aggregation
Description
Currently, the following index number methods are implemented:
-
laspey()for the Laspeyres index; -
paasche()for the Paasche index; -
toernq()for the Toernqvist index; -
fisher()for the Fisher index; -
walsh()for the Walsh index.
Function aggregate() uses these functions (or other functions provided by the user) for step-wise aggregation of lower-level subindices into the overall index following the COICOP hierarchy.
Usage
# bilateral price indices:
laspey(x, w0, wt=NULL)
paasche(x, w0=NULL, wt)
fisher(x, w0, wt)
toernq(x, w0, wt)
walsh(x, w0, wt)
# step-wise index aggregation:
aggregate(x, w0, wt, grp, index=laspey, add=list(), keep.lowest=TRUE)
Arguments
x |
numeric vector of price relatives obtained by |
w0, wt |
numeric vector of weights in the base period |
grp |
grouping variable to be used. These should be valid COICOP codes or other hierarchical codes. |
index |
a function or named list of functions specifying the index formula used for aggregation. Each function must have arguments |
add |
a named list of user-defined aggregates to be calculated. Each list element is a vector of ids that can be found in |
keep.lowest |
logical indicating if the lowest-level indices that form the base of all aggregation steps should be kept in the output. |
Value
Functions laspey(), paasche(), fisher(), toernq(), and walsh() return a single (aggregated) value.
Function aggregate() returns a data.table of aggregated values at each grp-level with the following variables:
| grp | character | the grouping variable | |
| is_aggregated | logical | is the value an aggregate (TRUE) or not; column available if keep.lowest=TRUE |
|
| w0, wt | numeric | sum of weights w0 and wt |
|
| index | numeric | aggregates for each index function
|
Author(s)
Sebastian Weinand
References
Eurostat (2024), Harmonised Index of Consumer Prices (HICP): Methodological Manual, Luxembourg: Publications Office of the European Union, online available at: https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/w/ks-gq-24-003.
See Also
Examples
library(data.table)
### EXAMPLE 1
# data for two times periods:
dt <- data.table(
"time"=rep(1:2, each=5),
"coicop"=rep(c("01111","01112","0112","0113","021"), times=2),
"price"=c(105,103,102,99,120, 105,104,110,98,125),
"weight"=rep(c(0.05,0.15,0.3,0.2,0.3), times=2),
"weight_lag"=rep(c(0.03,0.12,0.33,0.2,0.32), times=2))
# aggregate directly to overall index:
dt[, laspey(x=price, w0=weight), by="time"]
# gives identical results at top level as with stepwise
# aggregation through all coicop levels:
dt[, aggregate(x=price, w0=weight, grp=coicop, index=laspey), by="time"]
# this is no longer the case for the superlative indices as shown
# here for the walsh index:
dt[, walsh(x=price, w0=weight, wt=weight_lag), by="time"]
dt[, aggregate(x=price, w0=weight, wt=weight_lag, grp=coicop, index=walsh), by="time"]
# see also for example Auer and Wengenroth (2017, p. 2)
# apply user-defined function:
dt[, aggregate(x=price, w0=weight, grp=coicop,
index=list("carli"=function(x,w0=NULL,wt=NULL) mean(x))),
by="time"]
# add additional, user-defined aggregates (e.g. special aggregates):
dt[, aggregate(x=price, w0=weight, grp=coicop,
add=list("FOOD"=c("01111","021"), "MISS"=c("021","09"))),
by="time"]
### EXAMPLE 2: Index aggregation using published HICP data
# set cores for testing on CRAN:
library(restatapi)
options(restatapi_cores=1)
# import monthly price indices:
prc <- hicp.dataimport(id="prc_hicp_midx", filter=list(unit="I15", geo="EA"))
prc[, "time":=as.Date(paste0(time, "-01"))]
prc[, "year":=as.integer(format(time, "%Y"))]
setnames(x=prc, old="values", new="index")
# unchaining indices:
prc[, "dec_ratio" := unchain(x=index, t=time), by="coicop"]
# import item weights:
inw <- hicp.dataimport(id="prc_hicp_inw", filter=list(geo="EA"))
inw[, "time":=as.integer(time)]
setnames(x=inw, old=c("time","values"), new=c("year","weight"))
# derive coicop tree:
inw[grepl("^CP",coicop),
"tree":=tree(id=gsub("^CP","",coicop), w=weight, w.tol=0.1),
by=c("geo","year")]
# except for rounding, we receive total weight of 1000 in each period:
inw[tree==TRUE, sum(weight), by="year"]
# merge price indices and item weights:
hicp.data <- merge(x=prc, y=inw, by=c("geo","coicop","year"), all.x=TRUE)
hicp.data <- hicp.data[year <= year(Sys.Date())-1 & grepl("^CP\\d+", coicop),]
hicp.data[, "coicop" := gsub(pattern="^CP", replacement="", x=coicop)]
# compute all-items HICP:
hicp.own <- hicp.data[tree==TRUE,
list("laspey"=laspey(x=dec_ratio, w0=weight)),
by="time"]
setorderv(x=hicp.own, cols="time")
hicp.own[, "chain_laspey" := chain(x=laspey, t=time, by=12)]
hicp.own[, "chain_laspey_15" := rebase(x=chain_laspey, t=time, t.ref="2015")]
# add published all-items HICP for comparison:
hicp.own <- merge(
x=hicp.own,
y=hicp.data[coicop=="00", list(time, index)],
by="time",
all.x=TRUE)
plot(index-chain_laspey_15~time, data=hicp.own, type="l")
head(hicp.own[abs(index-chain_laspey_15)>0.1,])
# compute all-items HICP stepwise through all higher-levels:
hicp.own.all <- hicp.data[, aggregate(x=dec_ratio, w0=weight, grp=coicop, index=laspey),
by="time"]
setorderv(x=hicp.own.all, cols="time")
hicp.own.all[, "chain_laspey" := chain(x=laspey, t=time, by=12), by="grp"]
hicp.own.all[, "chain_laspey_15" := rebase(x=chain_laspey, t=time, t.ref="2015"), by="grp"]
# add published indices for compariosn:
hicp.own.all <- merge(
x=hicp.own.all,
y=hicp.data[, list(time,"grp"=coicop,index,weight)],
by=c("time","grp"),
all.x=TRUE)
hicp.own.all[, "diff" := index-chain_laspey_15]
head(hicp.own.all[abs(diff)>0.1,])
head(hicp.own.all[abs(w0-weight)>0.1,])
# compare all-items HICP from direct and step-wise aggregation:
agg.comp <- merge(
x=hicp.own.all[grp=="00", list(time, "index_stpwse"=chain_laspey_15)],
y=hicp.own[, list(time, "index_direct"=chain_laspey_15)],
by="time")
# no differences -> consistent in aggregation:
head(agg.comp[abs(index_stpwse-index_direct)>1e-4,])