Generalized Price and Quantity Indexes

A small R package for calculating lots of different price indexes, and by extension quantity indexes. Provides tools to build and work with any type of generalized bilateral index (of which most price indexes are), along with a few important indexes that donâ€™t belong to the generalized family.

Installation

``install.packages("gpindex")``

The development version can be installed from GitHub.

``devtools::install_github("marberts/gpindex")``

Usage

``````library(gpindex)

price6
#>   t1  t2  t3  t4  t5
#> 1  1 1.2 1.0 0.8 1.0
#> 2  1 3.0 1.0 0.5 1.0
#> 3  1 1.3 1.5 1.6 1.6
#> 4  1 0.7 0.5 0.3 0.1
#> 5  1 1.4 1.7 1.9 2.0
#> 6  1 0.8 0.6 0.4 0.2
quantity6
#>    t1  t2  t3  t4   t5
#> 1 1.0 0.8 1.0 1.2  0.9
#> 2 1.0 0.9 1.1 1.2  1.2
#> 3 2.0 1.9 1.8 1.9  2.0
#> 4 1.0 1.3 3.0 6.0 12.0
#> 5 4.5 4.7 5.0 5.6  6.5
#> 6 0.5 0.6 0.8 1.3  2.5

p0 <- price6[[1]]
p1 <- price6[[2]]
p2 <- price6[[3]]
q0 <- price6[[1]]
q1 <- price6[[2]]

# Calculate a Laspeyres and Paasche index
index_laspeyres(p1, p0, q0)
#> [1] 1.4
index_paasche(p1, p0, q1)
#> [1] 1.811905

# Can also be done if only weights are available
s0 <- index_weights("Laspeyres")(p0, q0)
s1 <- index_weights("Paasche")(p1, q1)

mean_arithmetic(p1 / p0, s0)
#> [1] 1.4
mean_harmonic(p1 / p0, s1)
#> [1] 1.811905

# Chain the Laspeyres index by price-updating the weights
mean_arithmetic(p1 / p0, s0) * mean_arithmetic(p2 / p1, weights_update(p1 / p0, s0))
#> [1] 1.05

index_laspeyres(p2, p0, q0)
#> [1] 1.05

# Get quote contributions for the Paasche index
contributions_harmonic(p1 / p0, s1)
#> [1]  0.02857143  0.71428571  0.04642857 -0.02500000  0.06666667 -0.01904762

# Also works for more exotic indexes, like the Lloyd-Moulton index
index_lm(p1, p0, q0, 0.5) # elasticity of 0.5
#> [1] 1.315599
mean_generalized(0.5)(p1 / p0, s0)
#> [1] 1.315599
mean_generalized(0.5)(p1 / p0, s0) * mean_generalized(0.5)(p2 / p1, weights_factor(0.5)(p1 / p0, s0))
#> [1] 1.003433
index_lm(p2, p0, q0, 0.5)
#> [1] 1.003433
contributions(0.5)(p1 / p0, s0)
#> [1]  0.03361012  0.26178360  0.04942922 -0.05699229  0.06468830 -0.03691970

# Calculate a Fisher index over 5 periods
mapply(index_fisher, price6, price6[1], quantity6, quantity6[1])
#>       t1       t2       t3       t4       t5
#> 1.000000 1.401050 1.272099 1.176163 1.071172

# Calculate a two-level index with different formulas
# Split data by even and odd rows
(prices <- split(p1 / p0, c("even", "odd")))
#> \$even
#> [1] 1.2 1.3 1.4
#>
#> \$odd
#> [1] 3.0 0.7 0.8

# Odd groups get a weight of 30% and even group gets 70%
weight <- c(0.3, 0.7)

# Calculate lower-level indexes
(lower <- sapply(prices, mean_geometric))
#>     even      odd
#> 1.297431 1.188784

# Calculate upper-level index
(upper <- mean_arithmetic(lower, weight))
#> [1] 1.221378

# Calculate quote contributions for lower-level indexes
(con_lower <- lapply(prices, contributions_geometric))
#> \$even
#> [1] 0.06927979 0.09986815 0.12828288
#>
#> \$odd
#> [1]  0.39113201 -0.12438246 -0.07796516

# Calculate quote contributions for upper-level index
(con_upper <- mapply("*", con_lower, weight))
#>            even         odd
#> [1,] 0.02078394  0.27379241
#> [2,] 0.02996044 -0.08706772
#> [3,] 0.03848486 -0.05457561``````