You could use an indicator for Public v Private, so your vars would be
Id Mode Choice Cost Public
32 1 1 120 0
32 2 0 20 1
-----Original Message-----
From: Julia Gamas [mailto:[email protected]]
Sent: Wednesday, January 19, 2005 7:43 AM
To: [email protected]
Subject: st: More on clogit: two questions
Hi again,
the reason I've separated cost into public and private is because I'm
assuming
that they are perceived differently by the user (Ben-Akiva&Lerman 1985 have
an
example of such a model in their text book). So my question would then be,
how
CAN I include alternative specific variables into the conditional logit
model
in Stata?
Thanks for any help.
Julia
> Date: Tue, 18 Jan 2005 10:24:57 -0500
> From: Julia Gamas <[email protected]>
> Subject: st: clogit: two questions
>
> Dear all,
> I have two questions about clogit: one, about whether or not I "fed" the
> data
> correctly to Stata, the second, about the sign on my coefficients. I do
> appologize if there is a simple answer that I overlooked.
>
> 1. I've "fed" data that is alternative specific to find the choice=1 of
> using
> private transportation versus choice=0 of using public transit. I have
> sociodemographic variables and generic variables too, but my concern is
with
> the alternative specific variable of mode cost. The format I used was the
> following for all ID's, can anybody tell me if this is wrong and if so,
how
> to
> fix it (I couldn't find more information in the Stata manual that might
> help).
>
> Id Mode Choice Car-Cost Public-transit-Cost
> 32 1 1 120 0
> 32 2 0 0 20
>
>
> Did I do the right thing by putting in the cost of using a car when the
mode
> is
> a car and zero in car cost when the mode is public?
>
> 2. I obtain a POSITIVE coefficient from public transit cost. My
> interpretation
> is that the more public tranist costs, the less we are likely to want to
use
> it
> and we may substitute to a car. I interpret this assuming that the model
is
> estimating V1-V2 (utility of driving - utility of using public transit) as
> in
> the logit equations. This would imply that the coefficient for public
> transit
> cost is negative in the utility of using public transit, but changes sign
to
> positive when we subtract that utility from utility of driving. I wanted
to
> check with you if this interpretation is correct, or do I, in fact, have
the
> wrong sign?
>
> Any help you can give me would be immensely appreciated.
>
> Sincerely,
>
> Julia A. Gamas
> Mexico City Project, EAPS
> 77 Massachusetts Avenue 54-1823
> Cambridge, MA 02139
>
> Date: Tue, 18 Jan 2005 10:01:21 -0600
> From: Leonelo Bautista <[email protected]>
> Subject: st: RE: clogit: two questions
>
> Julia,
> - -clogit- is used for matched data. I guess your ID variable identifies
the
> matching pair and that you are using this variable in the -group- option
> - -group(id))-. I think you should have only one variable for
transportation
> cost. So, for the first subject of the pair cost==120 and for the second
> subject cost==20. In the way you have your data now, the outcome variable
> (mode) is completely identifiable by the independent variables (all
choice=1
> have public cost=0 and all choice=0 have private cost=0). I don't think
you
> can get meaningful results in this way.
>
> Leonelo Bautista
>
>
> Date: Tue, 18 Jan 2005 11:38:34 -0500
> From: Julia Gamas <[email protected]>
> Subject: st: clogit two question addendum-code
>
> Dear all,
> someone asked that I post the code I used. It is a long program so I'm
> pasting
> in the relevant parts:
>
> . /*Total private*/
> . generate privatecost=0
>
> . replace privatecost=parkingcost+taxicost+carcost if tmode==1
> (23856 real changes made, 4 to missing)
>
> . save Logitmodes, replace
> file Logitmodes.dta saved
>
> . /*Total cost of using public transit:*/
> . generate publiccost=0
>
> . replace publiccost=(vp40_6+vp40_8+vp40_9+vp40_10)/100 if tmode==2
> (11888 real changes made)
>
> . save Logitmodes, replace
> file Logitmodes.dta saved
>
> ************
>
> . generate pphhpriv=pphh*private
>
> . generate en1priv=en1*private
>
> . generate en2priv=en2*private
>
> . generate incpppriv=incpp*private
>
> . generate vehicspriv=privehics*private
>
> . generate locpriv=location*private
>
> . generate hhincpriv=survhhinc*private
>
> . generate sexpriv=sex*private
>
> . generate headpriv=hofh*private
>
> . generate emppriv=empl*private
>
> **********************************
>
> . clogit choice private incpppriv vehicspriv pphhpriv locpriv blckpriv
> sexpriv
> headpriv emppriv pub
> > lictime privatetime, group (numob)
> note: 7614 groups (7614 obs) dropped due to all positive or
> all negative outcomes.
>
> Iteration 0: log likelihood = -23280.907
> Iteration 1: log likelihood = -17006.85
> Iteration 2: log likelihood = -16748.949
> Iteration 3: log likelihood = -16745.97
> Iteration 4: log likelihood = -16745.968
> Iteration 5: log likelihood = -16745.968
>
> Conditional (fixed-effects) logistic regression Number of obs =
> 68660
> LR chi2(11) =
> 14099.55
> Prob > chi2 =
> 0.0000
> Log likelihood = -16745.968 Pseudo R2 =
> 0.2963
>
> -
>
----------------------------------------------------------------------------
--
> choice | Coef. Std. Err. z P>|z| [95% Conf.
> Interval]
> -
>
-------------+--------------------------------------------------------------
--
> private | -.3507557 .0679412 -5.16 0.000 -.483918
> -.2175933
> incpppriv | .0048378 .0005038 9.60 0.000 .0038504
> .0058252
> vehicspriv | 1.362893 .0289785 47.03 0.000 1.306096
> 1.41969
> pphhpriv | -.2289017 .0083926 -27.27 0.000 -.2453508
> -.2124525
> locpriv | -.147314 .029848 -4.94 0.000 -.205815
> -.088813
> blckpriv | -.0042336 .0005498 -7.70 0.000 -.0053111
> -.0031561
> sexpriv | -.1134412 .0298782 -3.80 0.000 -.1720013
> -.0548811
> headpriv | 1.589408 .0398303 39.90 0.000 1.511342
> 1.667474
> emppriv | .8384917 .0303313 27.64 0.000 .7790434
> .8979399
> publictime | .0041342 .0006889 6.00 0.000 .002784
> .0054844
> privatetime | -.0099765 .0007839 -12.73 0.000 -.0115128
> -.0084401
> -
>
----------------------------------------------------------------------------
--
>
> . clogit, or
>
> Conditional (fixed-effects) logistic regression Number of obs =
> 68660
> LR chi2(11) =
> 14099.55
> Prob > chi2 =
> 0.0000
> Log likelihood = -16745.968 Pseudo R2 =
> 0.2963
>
> -
>
----------------------------------------------------------------------------
--
> choice | Odds Ratio Std. Err. z P>|z| [95% Conf.
> Interval]
> -
>
-------------+--------------------------------------------------------------
--
> private | .7041558 .0478412 -5.16 0.000 .6163637
> .8044525
> incpppriv | 1.00485 .0005062 9.60 0.000 1.003858
> 1.005842
> vehicspriv | 3.90748 .113233 47.03 0.000 3.691732
> 4.135836
> pphhpriv | .7954067 .0066755 -27.27 0.000 .78243
> .8085987
> locpriv | .8630229 .0257595 -4.94 0.000 .8139836
> .9150167
> blckpriv | .9957754 .0005474 -7.70 0.000 .994703
> .9968489
> sexpriv | .8927567 .0266739 -3.80 0.000 .8419781
> .9465977
> headpriv | 4.900848 .1952023 39.90 0.000 4.532811
> 5.298768
> emppriv | 2.312876 .0701525 27.64 0.000 2.179387
> 2.454541
> publictime | 1.004143 .0006917 6.00 0.000 1.002788
> 1.005499
> privatetime | .9900731 .0007761 -12.73 0.000 .9885532
> .9915954
> -
>
----------------------------------------------------------------------------
--
>
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