Description Objects from the Class Slots Extends Methods Author(s) Examples

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

.

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.

Class `CES`

, directly.
Class `Logit`

, by class `CES`

, distance 2.
Class `Bertrand`

, by class `Logit`

, distance 3.
Class `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.

`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.

Charles Taragin [email protected]

1 2 | ```
showClass("CESNests") # get a detailed description of the class
showMethods(classes="CESNests") # show all methods defined for the class
``` |

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