Dear Statalist
The questions concerns which model to use when we include cross-effects in a
choice experiment with two alternatives and an opt-out? So the first choice
is whether to opt-out or to make a choice, and the next choice is then
between alternative A and B.
We have run a nested logit model without cross-effects like the one shown
below:
. nlogitgen product = alt(prod_valg: 1 | 2, ingen: 3)
. nlogittree alt product
. gen hinc_pv= (product == 1)*hinc
. nlogit valg (alt = opdraet pris) (product = hinc_pv), group(resp_cs)
The test of homoskedasticity is strongly significant, which indicate that we
should use the nested logit model.
But when we include the cross-effects like income it shows something else:
.nlogit valg (alt = opdraet hinc3_opdraet hinc4_opdraet pris hinc2_pris
hinc3_pris hinc4_pris ) (product = hinc_pv), group(resp_cs)
Now the homoskedasticity test is not significant, and we should not use a
nested logit model, but a conditional logit
instead cf. Stata manual [R] nested logit p. 70 bottompage.
Now the question is which model should we use to model our survey (discrete
choice survey, with to alternatives pr. choice set, and an opt-out
included)? And are we including the cross-effects the right way?
Regards
Morten M�rkbak
Danish Research Institute of Food Economics Agricultural Policy
Research Division Rolighedsvej 25, DK-1958 Copenhagen
Email: [email protected], homepage: www.foi.dk
phone: +4535286869, Fax:+4535286801
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