ssa_calibrate_gnl: Calibrate second score auction model with GNL demand

In mergersim: Merger Simulation and Calibration

Calibrate second score auction model with GNL demand

Description

Calibrate second score auction model with GNL demand

Usage

ssa_calibrate_gnl(
  param,
  own,
  price,
  shares,
  cost,
  weight,
  nest_allocation,
  mu_constraint_matrix = NA
)

Arguments

param	Vector of demand parameters (alpha,mu)
own	Ownership matrix
price	Observed prices
shares	Observed market shares
cost	Marginal costs for each product
weight	Weighting matrix of dimensions J-by-J
nest_allocation	For generalized nested logit demand, a J-by-K matrix where each element (j,k) designates the membership of good j in nest k. Rows should sum to 1.
mu_constraint_matrix	is a (K-by-K') matrix indicating which nesting parameters are constrained to be equal to each other, where K is the number of nests and K' is the number of freely varying nesting parameters. mu_full = mu_constraint_matrix %*% mu_prime. Where mu_full is a vector of length K of the nesting parameter value for each nest, and mu_prime is a vector of length K' of parameters to be calculated. It must be the case that K is greater than K'.

Details

This function calibrates a second score auction model with generalized nested logit (GNL) demand

Value

Difference between model predicted and observed values of prices, shares, and diversions.

Examples

nest1 <- matrix( c(1, 0, 0, 0, 1, 1), ncol = 2, nrow = 3)

ssa_calibrate_gnl(param = c(-0.9, 1, 1),
own = diag(3),
price = c(.05, .34, .33),
shares = c( 0.31, 0.27, 0.25),
cost = c(.05,.31,.30),
weight = diag(c(1,1,1)),
nest_allocation = nest1,
mu_constraint_matrix = NA)

mergersim documentation built on July 21, 2026, 5:09 p.m.