I am currently working on the "Attribute Non-Attendance" problem and found your paper “Hess, Hensher (2010) - Using conditioning on observed choices to retrieve individual-specific attribute processing strategies”. I find the methodology interesting, because I do not need extra questions, e.g. self-reporting questions, to assign respondents to one of the two classes "Did ignore the attribute" or "Did not ignore the attribute". However, the model in the paper is a comparatively simple MIXL model, which was estimated in preference space.
I estimated my model using Apollo and HB in WTP space, including covariates. Now I wonder if and how I can apply the methodology in WTP space. For example, the utility of alternative 1 looks like this:
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V[['alt1']] = b_Preis_value * ( wtp_asc_1_value + wtp_Anbieter2_value * Anbieter2.1 + wtp_Anbieter3_value * Anbieter3.1 +
wtp_Strommix2_value * Strommix2.1 + wtp_Strommix3_value * Strommix3.1 + wtp_Strommix4_value * Strommix4.1 +
wtp_Regioanteil2_value * Regioanteil2.1 + wtp_Regioanteil3_value * Regioanteil3.1 +
Preis.1)
With the covariates, on the other hand, I imagine it to be simple, since they are fixed and only enter into the parameter estimation additively. For example:
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wtp_Strommix2_value = wtp_Strommix2 +
## Sociodemographics
wtp_Gender_Strommix2 * COV_Gender +
wtp_Age_Strommix2 * COV_Age +
wtp_Education_Strommix2 * COV_Education +
wtp_Residence_Strommix2 * COV_Residence
Many greetings
Nico