piar

piar: Price Index Aggregation in R - Published in JOSS (2024)

https://github.com/marberts/piar

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Price index aggregation in R

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  out.width = "100%"
)
```

# Price Index Aggregation in R 


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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) / PPI manual (2004) 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.

```{r, eval=FALSE}
install.packages("piar")
```

The development version can be installed from R-Universe

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

or directly from Github.

```{r, eval=FALSE}
pak::pak("marberts/piar")
```

## Usage

There are several 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.

```{r}
library(piar)

# Make Jevons business-level elemental indexes

head(ms_prices)

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

elementals
```

And an aggregation structure.

```{r}
# Make an aggregation structure from businesses to higher-level
# industrial classifications

ms_weights

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

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

pias
```

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.

```{r}
# Aggregate elemental indexes with an arithmetic index

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

# Chain them to get a time series

chain(index)
```

## 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.

ILO, IMF, UNECE, OECD, and World Bank. (2004). *Producer Price Index Manual: Theory and Practice*. International Monetary Fund.

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.

Owner

JOSS Publication

piar: Price Index Aggregation in R

Published

September 06, 2024

DOI

10.21105/joss.06781

Volume 9, Issue 101, Page 6781

Authors

Steve Martin
Statistics Canada, Ottawa, ON, Canada

Editor

Chris Vernon

Tags

economics inflation official statistics

View PDF Review Thread Software Archive

Citation (CITATION.cff)

cff-version: "1.2.0"
authors:
- family-names: Martin
  given-names: Steve
  orcid: "https://orcid.org/0000-0003-2544-9480"
doi: 10.5281/zenodo.13323298
message: If you use this software, please cite our article in the
  Journal of Open Source Software.
preferred-citation:
  authors:
  - family-names: Martin
    given-names: Steve
    orcid: "https://orcid.org/0000-0003-2544-9480"
  date-published: 2024-09-06
  doi: 10.21105/joss.06781
  issn: 2475-9066
  issue: 101
  journal: Journal of Open Source Software
  publisher:
    name: Open Journals
  start: 6781
  title: "piar: Price Index Aggregation in R"
  type: article
  url: "https://joss.theoj.org/papers/10.21105/joss.06781"
  volume: 9
title: "piar: Price Index Aggregation in R"

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