Hello,
I'm afraid this is not strictly a stata question. I have conducted a Discrete choice experiment. In this choice experiment there are three alternatives in each choice set. Alternatives 1 and 2 are characterized by 2 attributes with 3 levels and a payment attribute. Alternative 3 is fixed "opt out". The design is a D-efficient design.
In order to properly estimate the ASC I have decided to use effects coding. For simplification, I only present information relative to one attribute plus the payment attribute.
For my first attribute Nature that has three levels (L1, L2 and L3), I have created two variables, nature_l1 and nature_l2 . These variables are coded in the following manner:
- nature_l1 takes value 1 if the alternative is Nature L1 , value 0 if the alternative Nature L2 and value -1 if the alternative is Nature L3.
- nature_l2 takes value 0 if the alternative is Nature L1 , value 1 if the alternative Nature L2 and value -1 if the alternative is Nature L3.
I use a mixlogit model to estimate respondent's choice.=:
mixlogit choice, group(choiceset) id(id) nrep(500) rand(nature_l1 nature_L2 ASC Payment)
I want to estimate the WTP in preference space first. I wonder how I should do this?
For example, if I want to estimate the marginal WTP between Nature L1 and Nature L3. Let's consider that b1 and b2 are the respective mean values of the coefficients of nature_l1 and nature_l2 and b3 the coefficient of the payment attribute in the mixlogit results.
My impression is that I should calculate this marginal WTP with the following formula: (2b1+b2)/b3.
Is this the correct approach?
How could this be estimated in wtp space?
Thanks
Best,
I'm afraid this is not strictly a stata question. I have conducted a Discrete choice experiment. In this choice experiment there are three alternatives in each choice set. Alternatives 1 and 2 are characterized by 2 attributes with 3 levels and a payment attribute. Alternative 3 is fixed "opt out". The design is a D-efficient design.
In order to properly estimate the ASC I have decided to use effects coding. For simplification, I only present information relative to one attribute plus the payment attribute.
For my first attribute Nature that has three levels (L1, L2 and L3), I have created two variables, nature_l1 and nature_l2 . These variables are coded in the following manner:
- nature_l1 takes value 1 if the alternative is Nature L1 , value 0 if the alternative Nature L2 and value -1 if the alternative is Nature L3.
- nature_l2 takes value 0 if the alternative is Nature L1 , value 1 if the alternative Nature L2 and value -1 if the alternative is Nature L3.
I use a mixlogit model to estimate respondent's choice.=:
mixlogit choice, group(choiceset) id(id) nrep(500) rand(nature_l1 nature_L2 ASC Payment)
I want to estimate the WTP in preference space first. I wonder how I should do this?
For example, if I want to estimate the marginal WTP between Nature L1 and Nature L3. Let's consider that b1 and b2 are the respective mean values of the coefficients of nature_l1 and nature_l2 and b3 the coefficient of the payment attribute in the mixlogit results.
My impression is that I should calculate this marginal WTP with the following formula: (2b1+b2)/b3.
Is this the correct approach?
How could this be estimated in wtp space?
Thanks
Best,
Comment