antitrust (version 0.99.10)

CESNests-class: Class “CESNests”

Description

The “CESNests” class contains all the information needed to calibrate a nested CES demand system and perform a merger simulation analysis under the assumption that firms are playing a differentiated products Bertrand pricing game.

Objects from the Class

Objects can be created by using the constructor function ces.nests.

Slots

Let k denote the number of products produced by all firms.

nests:

A length k vector identifying the nest that each product belongs to.

parmsStart:

A length k vector who elements equal an initial guess of the nesting parameter values.

constraint:

A length 1 logical vector that equals TRUE if all nesting parameters are constrained to equal the same value and FALSE otherwise. Default is TRUE.

Extends

Class '>CES, directly. Class '>Logit, by class '>CES, distance 2. Class '>Bertrand, by class '>Logit, distance 3. Class '>Antitrust, by class '>Bertrand, distance 4.

Methods

For all of methods containing the ‘preMerger’ argument, ‘preMerger’ takes on a value of TRUE or FALSE, where TRUE invokes the method using the pre-merger ownership structure, while FALSE invokes the method using the post-merger ownership structure.

calcShares

signature(object, preMerger = TRUE, revenue = FALSE)

Compute either pre-merger or post-merger equilibrium revenue shares under the assumptions that consumer demand is nested CES and firms play a differentiated product Bertrand Nash pricing game. ‘revenue’ takes on a value of TRUE or FALSE, where TRUE calculates revenue shares, while FALSE calculates quantity shares.

calcSlopes

signature(object)

Uncover nested CES demand parameters. Assumes that firms are currently at equilibrium in a differentiated product Bertrand Nash pricing game.

CV

signature(object, revenueInside)

Calculates compensating variation. If ‘revenueInside’ is missing, then CV returns compensating variation as a percent of the representative consumer's income. If ‘revenueInside’ equals the total expenditure on all products inside the market, then CV returns compensating variation in levels.

elast

signature(object, preMerger = TRUE)

Computes a k x k matrix of own and cross-price elasticities.

Examples

# NOT RUN {
showClass("CESNests")           # get a detailed description of the class
showMethods(classes="CESNests") # show all methods defined for the class
# }