The “CESNests” class contains all the information needed to calibrate a nested CES demand system and perform a merger 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
Let k denote the number of products produced by all firms.
A length k vector identifying the nest that each product belongs to.
A length k vector who elements equal an initial guess of the nesting parameter values.
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.
Logit, by class
CES, distance 2.
Bertrand, by class
Logit, distance 3.
Antitrust, by class
Bertrand, distance 4.
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.
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.
Uncover nested CES demand parameters. Assumes that firms are currently at equilibrium in a differentiated product Bertrand Nash pricing game.
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.
signature(object, preMerger = TRUE)
Computes a k x k matrix of own and cross-price elasticities.
Charles Taragin email@example.com
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