pricelevels (version 1.4.0)

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())
geoyoung(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 positive 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 Section '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 by lowe(), young(), and geoyoung(). 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 removed from the data. Duplicated observations for r and n are aggregated, that is, duplicated prices p and weights w are averaged and duplicated quantities q added up. If there is more than one region in the data, products with prices in only one region r are removed.

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 index with base region 1:
dt[, jevons(p=price, r=region, n=product, base="1")]

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