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st: RE: Double bounded WTP
"Mentzakis, Emmanouil" <email@example.com>
st: RE: Double bounded WTP
Tue, 4 Dec 2007 22:19:40 -0000
Rather than writing your own code you can use a very similar model, estimated by -intreg- (which though assumes normal distribution).
Those who answer NN are left censored, those with YY are right censored and those with YN or NY fall within an interval.
In the model you ask for, you estimate a coefficient for the bid and the sigma is given by 1/cons.
In -intreg- you model the bids explicitly and so there is no coefficient for them. However, you can get the mean WTP by obtaining the linear prediction after the estimation.
From: firstname.lastname@example.org on behalf of Henrik Andersson
Sent: Tue 12/4/2007 9:31 PM
Subject: st: Double bounded WTP
I would to maximize the likelihood function for interval data from a dichotomous stated preference study. In this kind of study respondents are first asked if they are accepting to pay a certain bid (bid0). Depending on their answer they are then asked whether they accept a second higher bid (bid1) if they answered yes to the first question and a second lower bid if they answered no (bid 2).
My log likelihood is made up of four parts and is given by
YY = variable that defines respondent who has answered yes to initial and follow-up bid
NN = variable that defines respondent who has answered no to initial and follow-up bid
YN = variable that defines respondent who has answered yes to initial but no follow-up bid
NY = variable that defines respondent who has answered no to initial but yes follow-up bid
bid0=initial bid level
bid1= follow-up bid (2*bid0)
bid2= follow-up bid (bid0/2)
X = other covariates
a, b, and c = parameters
The variables YY, NN, YN, and NY can be defined as both dummies or a continuous variable. The intervals for the bid levels may overlap since the initial bid level is varied among respondents.
My experience from programming in Stata is very limited (close to nonexisting) so I don't know if what I want to do is trivial to solve, or if it is not possible to do. I would be very grateful for any help.
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