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From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: clogit for discrete choice experiment with multiple choice sets |

Date |
Mon, 30 Jan 2012 09:49:03 +0000 |

Thanks on behalf of the list to Klaus for giving full references. A pedantic correction: Cambridge University is not based in Cambridge, MA but in Cambridge, UK! Cambridge, MA is the seat of much younger universities. Nick On Mon, Jan 30, 2012 at 9:36 AM, Klaus Pforr <kpforr@googlemail.com> wrote: > <> > Dear Hadji, > > this seems to be an application for multilevel or panel multinomial logit. > There is a fixed effects model by Chamberlain (1980). The fixed effects are > in your case on the person level. Possible random effects solutions are > discussed in Train (2009). The first model has not been implemented yet (cf. > Allison 2009, p.44), but I'm am currently working on an ado for this model > (http://www.stata.com/meeting/germany11/desug11_pforr.pdf). The latter > models are complicated and can be estimated with GLM. > > There is a also back door solution for the fixed effects estimator for small > samples and short panel/small clusters (in your case the the number of > experiments). Börsch-Supan applied the Chamberlain model on housing choices > and rearranged the data in a way so that he could use the implemented clogit > to estimate the model. The data organisation is the following: In a > simplified version of your case you would have only 3 experiments (or panel > time points in the chamberlain lingo) and 3 alternatives. > Lets say you have the indiv 1 with this selection (this is example is > purposely simple) > xp choice > 1 1 > 2 2 > 3 3 > > When you look up the equation in the chamberlain model, you find the > conditional likelihood of the prob to choose the time series that was chosen > conditional ("i.e. divided by") the prob of all permutations of the chosen > alternatives. > > You look at all combination of choices, which have the same number of 1's, > 2's and 3's (or in general all of your outcomes) for the specific > individual. This set of permutation makes your set of alternatives: > > Permutaion Was it chosen? > 123 yes > 132 no > 213 no > 231 no > 312 no > 321 no > > After this reorganisation you run a clogit on the data with respondent as > group, and have the multinomial logit with fixed effects. This is very > cumbersome even your simple application, but it works. You also have to > think about how to generate you independet variable for this to get the > coefficents that you want. > > Here is the literature: > > Börsch-Supan, Axel. 1987. Econometric analysis of discrete choice: With > applications on the demand for housing in the U.S. and West-Germany. Berlin > et al.: Springer Verlag. > > Börsch-Supan, Axel. 1990. Panel data analysis of housing choices. Regional > science and urban economics 20: 65–82. > > Börsch-Supan, Axel, und Henry O. Pollakowski. 1990. Estimating housing > consumption adjustments from panel data. Journal of urban economics 27: > 131–150. > > Chamberlain, Gary. 1980. Analysis of Covariance with Qualitative Data. > Review of Economic Studies 57: 225–238. > > Train, Kenneth E. 2009. Discrete choice methods with simulation. 2. ed. > Cambridge, MA et al.: Cambridge University Press. > > I hope this helps > > best > > Klaus > > Am 28.01.2012 08:32, schrieb Hadji Cortez Jalotjot: >> >> Hi! >> >> I >> implemented a discrete choice experiment to model vehicle choice. In my >> questionnaire, I presented each respondents with 10 choice experiments >> or choice sets with each choice set having 3 alternatives or choices. >> >> The explanatory variables are the characteristics of the vehicles. With >> this, I am fitting a conditional logit model. >> >> In my data set, dummy variables were used to represent the explanatory >> variables. Since each choice experiment has 3 alternative options, each >> choice experiment corresponds to 3 rows of observations. So 10 choice >> experiments per respondent X 3 alternative options per choice >> experiments = 30 rows of observations per respondent. (sample data below >> shows only 3 choice experiments with >> some of the explanatory variables for respondent 1) >> >> >> respno choice_set choice var1a var1b var1c .. . .. >> none >> >> 1 1 1 1 0 0 >> 0 >> 1 1 0 0 0 1 >> 0 >> 1 1 0 0 0 0 >> 1 >> >> 1 2 0 0 1 0 >> 0 >> >> 1 2 1 1 0 0 >> 0 >> >> 1 2 0 0 0 0 >> 1 >> >> >> 1 3 0 0 0 1 >> 0 >> >> 1 3 0 1 0 0 >> 0 >> >> 1 3 1 0 0 0 >> 1 >> >> For clogit to work, I must select a variable that will identify the >> grouping for which the software will run the analysis. >> >> Now, for this kind of data in which respondents answered multiple choice >> sets (10 in my case), which should I used as a group? >> Is it the respno or choice_set? >> >> I am confused because if I use respno, Stata says multiple positve >> outcomes in a group. And the predicted probabilities is computed for >> the whole 30 alternative options and not only for the 3 alternative >> options per choice set. >> >> But if I use the choice_set as the grouping and I extend the model to >> include respondent characteristics (e.g. income), I may have problem >> with fixed effects because for example choice_set 1 and choice_set 2 is >> from the same respondent and therefore will have exactly the same >> income. >> * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: clogit for discrete choice experiment with multiple choice sets***From:*Klaus Pforr <kpforr@googlemail.com>

**References**:**st: clogit for discrete choice experiment with multiple choice sets***From:*Hadji Cortez Jalotjot <jalotjot@yahoo.com>

**Re: st: clogit for discrete choice experiment with multiple choice sets***From:*Klaus Pforr <kpforr@googlemail.com>

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