Price Index Aggregation in R

Most price indexes are made with a two-step procedure, where period-over-period elemental indexes are first calculated for a collection of elemental aggregates at each point in time, and then aggregated according to a price index aggregation structure. These indexes can then be chained together to form a time series that gives the evolution of prices with respect to a fixed base period. This package contains a collection of functions that revolve around this work flow, making it easy to build standard price indexes, and implement the methods described by Balk (2008), von der Lippe (2007), and the CPI manual (2020) for bilateral price indexes.

The tools in this package are designed to be useful for both researching new sources of data and methods to construct price indexes, and the regular production of price statistics. It is targeted towards economists, statisticians, and data scientists working at national statistical agencies, central banks, financial institutions, and in academia that want to measure and study the evolution of prices over time.

Installation

Get the stable version from CRAN.

install.packages("piar")

The development version can be installed from R-Universe

install.packages("piar", repos = c("https://marberts.r-universe.dev", "https://cloud.r-project.org"))

or directly from Github.

pak::pak("marberts/piar")

Usage

There is a detailed vignette showing how to use piar: browseVignettes("piar"). But the basic work flow is fairly simple.

The starting point is to make period-over-period elemental price indexes with the elemental_index() function.

library(piar)

# Make Jevons business-level elemental indexes

head(ms_prices)
#>   period business product price
#> 1 202001       B1       1  1.14
#> 2 202001       B1       2    NA
#> 3 202001       B1       3  6.09
#> 4 202001       B2       4  6.23
#> 5 202001       B2       5  8.61
#> 6 202001       B2       6  6.40

elementals <- ms_prices |>
  transform(
    relative = price_relative(price, period = period, product = product)
  ) |>
  elemental_index(relative ~ period + business, na.rm = TRUE)

elementals
#> Period-over-period price index for 4 levels over 4 time periods 
#>    202001    202002    202003   202004
#> B1      1 0.8949097 0.3342939      NaN
#> B2      1       NaN       NaN 2.770456
#> B3      1 2.0200036 1.6353355 0.537996
#> B4    NaN       NaN       NaN 4.576286

And an aggregation structure.

# Make an aggregation structure from businesses to higher-level
# industrial classifications

ms_weights
#>   business classification weight
#> 1       B1             11    553
#> 2       B2             11    646
#> 3       B3             11    312
#> 4       B4             12    622
#> 5       B5             12    330

ms_weights[c("level1", "level2")] <-
  expand_classification(ms_weights$classification)

pias <- ms_weights[c("level1", "level2", "business", "weight")]

pias
#>   level1 level2 business weight
#> 1      1     11       B1    553
#> 2      1     11       B2    646
#> 3      1     11       B3    312
#> 4      1     12       B4    622
#> 5      1     12       B5    330

The aggregate() method can then be used to aggregate the elemental indexes according to the aggregation structure (the first three rows below) and fill in missing elemental indexes while maintaining consistency in aggregation. There are a variety of methods to work with these index objects, such as chaining them over time.

# Aggregate elemental indexes with an arithmetic index

index <- aggregate(elementals, pias, na.rm = TRUE)

# Chain them to get a time series

chain(index)
#> Fixed-base price index for 8 levels over 4 time periods 
#>    202001    202002    202003    202004
#> 1       1 1.3007239 1.3827662 3.7815355
#> 11      1 1.3007239 1.3827662 2.1771866
#> 12      1 1.3007239 1.3827662 6.3279338
#> B1      1 0.8949097 0.2991629 0.4710366
#> B2      1 1.3007239 1.3827662 3.8308934
#> B3      1 2.0200036 3.3033836 1.7772072
#> B4      1 1.3007239 1.3827662 6.3279338
#> B5      1 1.3007239 1.3827662 6.3279338

Contributing

All contributions are welcome. Please start by opening an issue on GitHub to report any bugs or suggest improvements and new features. See the contribution guidelines for this project for more information.

References

Balk, B. M. (2008). Price and Quantity Index Numbers. Cambridge University Press.

Chiru, R., Huang, N., Lequain, M. Smith, P., and Wright, A. (2015). The Canadian Consumer Price Index Reference Paper, Statistics Canada catalogue 62-553-X. Statistics Canada.

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

von der Lippe, P. (2007). Index Theory and Price Statistics. Peter Lang.