geks: GEKS index

Description

Calculate a generalized inter-temporal GEKS price index over a rolling window.

Usage

geks(f, r = 0)
tornqvist_geks(
  p,
  q,
  period,
  product,
  window = nlevels(period),
  n = window - 1L,
  na.rm = FALSE,
  match_method = c("all", "back-price")
)
fisher_geks(
  p,
  q,
  period,
  product,
  window = nlevels(period),
  n = window - 1L,
  na.rm = FALSE,
  match_method = c("all", "back-price")
)
walsh_geks(
  p,
  q,
  period,
  product,
  window = nlevels(period),
  n = window - 1L,
  na.rm = FALSE,
  match_method = c("all", "back-price")
)

Value

geks() returns a function:

function(p,
         q,
         period,
         product,
         window = nlevels(period),
         n = window - 1,
         na.rm = FALSE,
         match_method = c("all", "back-price")){...}

This calculates a period-over-period GEKS index with the desired index-number formula, returning a list for each window with a named-numeric vector of index values.

tornqvist_geks(), fisher_geks(), and walsh_geks() each return a list with a named numeric vector giving the value of the respective period-over-period GEKS index for each window.

Arguments

f: A price index function that uses information on both base and current-period prices and quantities, and satisfies the time-reversal test. Usually a Törnqvist, Fisher, or Walsh index.
r: A finite number giving the order of the generalized mean used to average price indexes over the rolling window. The default uses a geometric mean.
p: A numeric vector of prices, the same length as q.
q: A numeric vector of quantities, the same length as p.
period: A factor, or something that can be coerced into one, that gives the corresponding time period for each element in p and q. The ordering of time periods follows the levels of period to agree with cut().
product: A factor, or something that can be coerced into one, that gives the corresponding product identifier for each element in p and q.
window: A positive integer giving the length of the rolling window. The default is a window that encompasses all periods in period. Non-integers are truncated towards zero.
n: A positive integer giving the length of the index series for each window, starting from the end of the window. For example, if there are 13 periods in window, setting n = 1 gives the index for period 13. The default gives an index for each period in window. Non-integers are truncated towards zero.
na.rm: Passed to f to control if missing values are removed.
match_method: Either 'all' to match all products against each other (the default) or 'back-price' to match only back prices. The later can be faster when there is lots of product imbalanced, but should be used with a balanced index-number formula f.

References

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

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

Ivancic, L., Diewert, W. E., and Fox, K. J. (2011). Scanner data, time aggregation and the construction of price indexes. Journal of Econometrics, 161(1): 24--35.

Examples

Run this code

price <- 1:10
quantity <- 10:1
period <- rep(1:5, 2)
product <- rep(letters[1:2], each = 5)

cumprod(tornqvist_geks(price, quantity, period, product)[[1]])

# Calculate the index over a rolling window.

(tg <- tornqvist_geks(price, quantity, period, product, window = 3))

# Use a movement splice to combine the indexes in each window.

splice_index(tg, 2)

# ... or use a mean splice.

splice_index(tg)

# Use all non-missing data.

quantity[2] <- NA
fisher_geks(price, quantity, period, product, na.rm = TRUE)

# Remove records with any missing data.

fg <- geks(balanced(fisher_index))
fg(price, quantity, period, product, na.rm = TRUE)

# Make a Jevons GEKS index.

jevons_geks <- geks(\(p1, p0, ..., na.rm) jevons_index(p1, p0, na.rm))
jevons_geks(price, quantity, period, product)