wtp: "Willingness to Pay" for fmlogit models

View source: R/wtp.R

wtpR Documentation

"Willingness to Pay" for fmlogit models

Description

Calculates the willingness to pay for fractional multinomial logit models.

Usage

wtp(object, wtp.vec, varlist = NULL, indv.obs = F)

Arguments

object

An "fmlogit.margins" object.

wtp.vec

A 1*J vector that contains the willingness to pay for each choice j.

varlist

A string vector which provides the name of variables to calculate the wtp. If missing, all variables in object will be calculated.

Details

This function calculates the aggregate effect of a variable on the "willingness to pay" by linearly multiplying the average partial effect with ex-ante (arbitary) willingness to pay numbers associated with each choice.

Suppose there are three choices A,B,C, each with a willingness to pay (or cost, profit, budget), of 100, 200, and 300. The discrete effect of variable X on A,B and C are 0.5, 0.5, and -1, with standard error 0.2, 0.3 and 0.5. The aggregated discrete effect of X on the total willingness to pay (or cost), is thus 100*0.5 + 200*0.5 + 300*(-1) = -150. And the standard error can be also calculated to be 162.8, assuming that the standard error is independent. A simple z-test is provided to test whether the aggregate effect is different from zero.

Note that if the input fmlogit.margins object has no standard error computation, then no standard error

Value

A matrix containing the estimates, standard error, z-stats, and p-value.

Examples

#results1 = fmlogit(y,X)
#effects1 = effects(results1,effect="marginal",se=T)
# assume that the WTP = 1,2,3,...J for each choice j. 
wtp(effects1,seq(1:nrow(effects1$effects))) 

f1kidd/fmlogit documentation built on June 26, 2022, 4:33 p.m.