CES-Functions: (Nested) Constant Elasticity of Substitution Demand Calibration and Merger Simulation

Description

Calibrates consumer demand using (Nested) Constant Elasticity of Substitution (CES) and then simulates the price effect of a merger between two firms under the assumption that all firms in the market are playing a differentiated products Bertrand pricing game.

Let k denote the number of products produced by all firms playing the Bertrand pricing game below.

Usage

ces(
  prices,
  shares,
  margins,
  diversions,
  ownerPre,
  ownerPost,
  normIndex = ifelse(sum(shares) < 1, NA, 1),
  mktElast = NA_real_,
  insideSize = NA_real_,
  mcDelta = rep(0, length(prices)),
  subset = rep(TRUE, length(prices)),
  priceOutside = 1,
  priceStart = prices,
  isMax = FALSE,
  control.slopes,
  control.equ,
  labels = paste("Prod", 1:length(prices), sep = ""),
  ...
)
ces.alm(
  prices,
  shares,
  margins,
  ownerPre,
  ownerPost,
  mktElast = NA_real_,
  insideSize = NA_real_,
  mcDelta = rep(0, length(prices)),
  subset = rep(TRUE, length(prices)),
  priceOutside = 1,
  priceStart = prices,
  isMax = FALSE,
  parmsStart,
  control.slopes,
  control.equ,
  labels = paste("Prod", 1:length(prices), sep = ""),
  ...
)
ces.nests(
  prices,
  shares,
  margins,
  ownerPre,
  ownerPost,
  nests = rep(1, length(shares)),
  normIndex = ifelse(sum(shares) < 1, NA, 1),
  insideSize = NA_real_,
  mcDelta = rep(0, length(prices)),
  subset = rep(TRUE, length(prices)),
  priceOutside = 1,
  priceStart = prices,
  isMax = FALSE,
  constraint = TRUE,
  parmsStart,
  control.slopes,
  control.equ,
  labels = paste("Prod", 1:length(prices), sep = ""),
  ...
)

Value

ces returns an instance of class CES. ces.alm returns an instance of class CESALM. ces.nests returns an instance of CESNests, a child class of CES.

Arguments

prices: A length k vector of product prices.
shares: A length k vector of product revenue shares.
margins: A length k vector of product margins, some of which may equal NA.
diversions: A k x k matrix of diversion ratios with diagonal elements equal to -1. Default is missing.
ownerPre: EITHER a vector of length k whose values indicate which firm produced a product pre-merger OR a k x k matrix of pre-merger ownership shares.
ownerPost: EITHER a vector of length k whose values indicate which firm produced a product after the merger OR a k x k matrix of post-merger ownership shares.
normIndex: An integer specifying the product index against which the mean values of all other products are normalized. Default is 1.
mktElast: a negative value indicating market elasticity. Default is NA.
insideSize: total revenues included in the market.
mcDelta: A vector of length k where each element equals the proportional change in a product's marginal costs due to the merger. Default is 0, which assumes that the merger does not affect any products' marginal cost.
subset: A vector of length k where each element equals TRUE if the product indexed by that element should be included in the post-merger simulation and FALSE if it should be excluded.Default is a length k vector of TRUE.
priceOutside: A length 1 vector indicating the price of the outside good. Default is 1.
priceStart: A length k vector of starting values used to solve for equilibrium price. Default is the ‘prices’ vector.
isMax: If TRUE, checks to see whether computed price equilibrium locally maximizes firm profits and returns a warning if not. Default is FALSE.
control.slopes: A list of optim control parameters passed to the calibration routine optimizer (typically the calcSlopes method).
control.equ: A list of BBsolve control parameters passed to the non-linear equation solver (typically the calcPrices method).
labels: A k-length vector of labels. Default is "Prod#", where ‘#’ is a number between 1 and the length of ‘prices’.
...: Additional options to feed to the BBsolve optimizer used to solve for equilibrium prices.
parmsStart: A vector of starting values used to solve for price coefficient and nest parameters. The first element should always be the price coefficient and the remaining elements should be nesting parameters. Theory requires the nesting parameters to be greater than the price coefficient. If missing then the random draws with the appropriate restrictions are employed.
nests: A length k vector identifying the nest that each product belongs to.
constraint: if TRUE, then the nesting parameters for all non-singleton nests are assumed equal. If FALSE, then each non-singleton nest is permitted to have its own value. Default is TRUE.

Author

Charles Taragin ctaragin+antitrustr@gmail.com

Details

Using product prices, revenue shares and all of the product margins from at least one firm, ces is able to recover the price coefficient and product mean valuations in a Constant Elasticity of Substitution demand model. ces then uses these calibrated parameters to simulate the price effects of a merger between two firms under the assumption that that all firms in the market are playing a differentiated products Bertrand pricing game.

ces.alm is identical to ces except that it assumes that an outside product exists and uses additional margin information to estimate the share of the outside good.

ces.nests is identical to ces except that it includes the ‘nests’ argument which may be used to assign products to different nests. Nests are useful because they allow for richer substitution patterns between products. Products within the same nest are assumed to be closer substitutes than products in different nests. The degree of substitutability between products located in different nests is controlled by the value of the nesting parameter sigma. The nesting parameters for singleton nests (nests containing only one product) are not identified and normalized to 1. The vector of sigmas is calibrated from the prices, revenue shares, and margins supplied by the user.

By default, all non-singleton nests are assumed to have a common value for sigma. This constraint may be relaxed by setting ‘constraint’ to FALSE. In this case, at least one product margin must be supplied from a product within each nest.

In both ces and ces.nests, if revenue shares sum to 1, then one product's mean value is not identified and must be normalized to 1. ‘normIndex’ may be used to specify the index (position) of the product whose mean value is to be normalized. If the sum of revenue shares is less than 1, both of these functions assume that the exists a k+1st product in the market whose price and mean value are both normalized to 1.

References

Anderson, Simon, Palma, Andre, and Francois Thisse (1992). Discrete Choice Theory of Product Differentiation. The MIT Press, Cambridge, Mass.

Epstein, Roy and Rubinfeld, Daniel (2004). “Effects of Mergers Involving Differentiated Products.”

Sheu G (2011). “Price, Quality, and Variety: Measuring the Gains From Trade in Differentiated Products.” U.S Department of Justice.

Examples

Run this code

## Calibration and simulation results from a merger between Budweiser and
## Old Style. Assume that typical consumer spends 1% of income on beer,
## and that total beer expenditure in US is 1e9
## Source: Epstein/Rubenfeld 2004, pg 80

prodNames <- c("BUD","OLD STYLE","MILLER","MILLER-LITE","OTHER-LITE","OTHER-REG")
ownerPre <-c("BUD","OLD STYLE","MILLER","MILLER","OTHER-LITE","OTHER-REG")
ownerPost <-c("BUD","BUD","MILLER","MILLER","OTHER-LITE","OTHER-REG")
nests <- c("R","R","R","L","L","R")

price    <- c(.0441,.0328,.0409,.0396,.0387,.0497)
shares   <- c(.071,.137,.251,.179,.093,.269)
margins  <- c(.3830,.5515,.5421,.5557,.4453,.3769)

names(price) <-
  names(shares) <-
  names(margins) <-
  prodNames

result.ces <-ces(price,shares,margins,ownerPre=ownerPre,ownerPost=ownerPost,
                 labels=prodNames)

print(result.ces)           # return predicted price change
summary(result.ces)         # summarize merger simulation

elast(result.ces,TRUE)      # returns premerger elasticities
elast(result.ces,FALSE)     # returns postmerger elasticities

diversion(result.ces,TRUE)  # return premerger diversion ratios
diversion(result.ces,FALSE) # return postmerger diversion ratios

cmcr(result.ces)            #calculate compensating marginal cost reduction
upp(result.ces)             #calculate Upwards Pricing Pressure Index

CV(result.ces)              #calculate compensating variation as a percent of
#representative consumer income

## Implement the Hypothetical Monopolist Test
## for BUD and OLD STYLE using a 5\% SSNIP

HypoMonTest(result.ces,prodIndex=1:2)


## Get a detailed description of the 'CES' class slots
showClass("CES")

## Show all methods attached to the 'CES' Class
showMethods(classes="CES")

## Show which class have their own 'elast' method
showMethods("elast")

## Show the method definition for 'elast' and Class 'CES'
getMethod("elast","CES")

Run the code above in your browser using DataLab