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Price indexes are usually made from several sources of data. An important benefit of the usual two-step workflow to make price indexes is that the elemental indexes can be built piecemeal—using different sources of data and different index-number formulas—and then aggregated with a consistent structure.
Let’s extend the example in vignette("piar") by having
an alternate source of data for business B5 that is always
missing in the ms_prices dataset.
library(piar)
# Make an aggregation structure.
ms_weights[c("level1", "level2")] <-
expand_classification(ms_weights$classification)
pias <- ms_weights[c("level1", "level2", "business", "weight")] |>
as_aggregation_structure()
# Make elemental index.
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 4.576286Instead of using survey-like data for the other businesses,
B5 is made from scanner-like data with many price and
quantity observations at each point in time.
set.seed(12345)
scanner_prices <- data.frame(
period = rep(c("201904", time(elementals)), each = 200),
product = 1:200,
price = round(rlnorm(5 * 200) * 10, 1),
quantity = round(runif(5 * 200, 100, 1000))
)
head(scanner_prices)## period product price quantity
## 1 201904 1 18.0 958
## 2 201904 2 20.3 660
## 3 201904 3 9.0 579
## 4 201904 4 6.4 903
## 5 201904 5 18.3 276
## 6 201904 6 1.6 896These type of data often require the use of a multilateral index like the GEKS. For the sake of illustration, we’ll make a Fisher GEKS index over a 3 quarter rolling window and use a mean splice to make a single time series.
library(gpindex)
geks_elementals <- with(
scanner_prices,
fisher_geks(price, quantity, period, product, window = 3)
) |>
splice_index() |>
t() |>
as_index(chainable = FALSE) |>
set_levels("B5") |>
rebase("202001")
geks_elementals## Fixed-base price index for 1 levels over 4 time periods
## 202001 202002 202003 202004
## B5 1 1.012081 1.143009 0.8109263These values can now be merged with the other elemental indexes, getting turned into a period-over-period index in the process, and then aggregated.
## Period-over-period price index for 8 levels over 4 time periods
## 202001 202002 202003 202004
## 1 1 1.1891575 1.0848816 2.1434597
## 11 1 1.3007239 1.0630743 1.5745154
## 12 1 1.0120809 1.1293651 3.2358970
## B1 1 0.8949097 0.3342939 1.5745154
## B2 1 1.3007239 1.0630743 2.7704563
## B3 1 2.0200036 1.6353355 0.5379960
## B4 1 1.0120809 1.1293651 4.5762862
## B5 1 1.0120809 1.1293651 0.7094664These binaries (installable software) and packages are in development.
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