pricelevels (version 1.3.0)

bilateral.index: Bilateral price indices

Description

Calculation of bilateral price indices. Currently, the following ones are implemented (see below in alphabetic order).

Usage

banerjee(p, r, n, q, base=NULL, settings=list())
bmw(p, r, n, base=NULL, settings=list())
carli(p, r, n, base=NULL, settings=list())
cswd(p, r, n, base=NULL, settings=list())
davies(p, r, n, q, base=NULL, settings=list())
drobisch(p, r, n, q, w=NULL, base=NULL, settings=list())
dutot(p, r, n, base=NULL, settings=list())
fisher(p, r, n, q, w=NULL, base=NULL, settings=list())
geolaspeyres(p, r, n, q, w=NULL, base=NULL, settings=list())
geopaasche(p, r, n, q, w=NULL, base=NULL, settings=list())
geowalsh(p, r, n, q, w=NULL, base=NULL, settings=list())
harmonic(p, r, n, base=NULL, settings=list())
jevons(p, r, n, base=NULL, settings=list())
laspeyres(p, r, n, q, w=NULL, base=NULL, settings=list())
lehr(p, r, n, q, base=NULL, settings=list())
lowe(p, r, n, q, base=NULL, settings=list())
medgeworth(p, r, n, q, base=NULL, settings=list())
paasche(p, r, n, q, w=NULL, base=NULL, settings=list())
palgrave(p, r, n, q, w=NULL, base=NULL, settings=list())
svartia(p, r, n, q, w=NULL, base=NULL, settings=list())
toernqvist(p, r, n, q, w=NULL, base=NULL, settings=list())
theil(p, r, n, q, w=NULL, base=NULL, settings=list())
uvalue(p, r, n, q, base=NULL, settings=list())
walsh(p, r, n, q, w=NULL, base=NULL, settings=list())
young(p, r, n, q, base=NULL, settings=list())

Value

A named vector of price levels.

Arguments

p

A numeric vector of prices.

r, n

A character vector or factor of regional entities r and products n, respectively.

q, w

A numeric vector of non-negative quantities q or expenditure share weights w (see details). Either q or w must be provided for weighted indices. If both q and w are provided, q will be used.

base

A character specifying the base region to which all price levels are expressed. If NULL, base region is set internally.

settings

A list of control settings to be used. The following settings are supported:

• chatty : A logical specifying if warnings and info messages should be printed or not. The default is getOption("pricelevels.chatty").

• connect : A logical specifying if the data should be checked for connectedness or not. The default is getOption("pricelevels.connect"). If the data are not connected, price levels are computed within the biggest block of connected regions or the block of regions to which the base region belongs. See also connect().

• plot : A logical specifying if the calculated price levels should be plotted or not. If TRUE, the price ratios of each region are displayed as boxplots and the price levels are added as colored points. The default is getOption("pricelevels.plot").

• qbase : A character specifying the region \(b\) whose quantities (and prices) should be used in lowe() and young(). If NULL, prices are averaged and quantities added up for each product, i.e. \(p_i^b=\sum_{r=1}^{R} p_i^r / R\) and \(q_i^b=\sum_{r=1}^{R} q_i^r\).

Author

Sebastian Weinand

Details

Before calculations start, missing values are excluded and duplicated observations for r and n are aggregated, that is, duplicated prices p and weights w are averaged and duplicated quantities q added up.

The weights w must represent expenditure shares defined as \(w_i^r = p_i^r q_i^r / \sum_{j=1}^{N} p_j^r q_j^r\). They are internally (re-)normalized such that they add up to 1 for each region r.

References

ILO, IMF, OECD, UNECE, Eurostat and World Bank (2020). Consumer Price Index Manual: Concepts and Methods. Washington DC: International Monetary Fund.

Examples

# sample complete price data:
set.seed(123)
dt1 <- rdata(R=3, B=1, N=5)

# compute jevons and toernqvist index:
dt1[, jevons(p=price, r=region, n=product, base="1")]
dt1[, toernqvist(p=price, r=region, n=product, q=quantity, base="1")]

# compute lowe index using quantities of region 2:
dt1[, lowe(p=price, r=region, n=product, q=quantity, base="1",
           settings=list(qbase="2"))]

# add price data:
dt2 <- rdata(R=4, B=1, N=4)
dt2[, "region":=factor(region, labels=4:7)]
dt2[, "product":=factor(product, labels=6:9)]
dt <- rbind(dt1, dt2)
dt[, is.connected(r=region, n=product)] # non-connected now

# compute jevons and toernqvist index:
dt[, jevons(p=price, r=region, n=product, base="1")]

# change base region:
dt[, jevons(p=price, r=region, n=product, base="4")]