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st: How to incorporate cross-effects in a nested logit model?

From   Morten M�rkbak <[email protected]>
To   "'[email protected]'" <[email protected]>
Subject   st: How to incorporate cross-effects in a nested logit model?
Date   Mon, 17 Jan 2005 10:13:48 +0100

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

. 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?


Morten M�rkbak
Danish Research Institute of Food Economics Agricultural Policy 
Research Division Rolighedsvej 25, DK-1958 Copenhagen 
Email: [email protected], homepage:
phone: +4535286869, Fax:+4535286801

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