help clogithet


clogithet -- Heteroscedastic conditional logit model.


clogithet depvar [indepvars] [if] [in] [weight] , group(varname) het( varlist) [options]

options Description ------------------------------------------------------------------------- Model * group(varname) matched group variable * het(varlist) independent variables to model the variance

SE/Robust robust robust standard errors cluster(varname) adjust standard errors for intragroup correlation opg standard errors based on the outer product of gradient matrix

Reporting lm report Lagrange multiplier test for heteroscedasticity clogit report (homoscedastic) conditional logit estimates

------------------------------------------------------------------------- * group(varname) and het(varlist) are required. fweights, iweights, and pweights are allowed (see weight), but they are interpreted to apply to groups as a whole, not to individual observations.


clogithet fits a heteroscedastic version of McFadden's conditional logit model. This model is also referred to as the parametrised heteroscedastic multinomial logit model (Hensher et al., 1999) and the heteroscedastic logit model (DeShazo and Fermo, 2002; Hole, 2006).

Like hetprob, clogithet models the relationship between the error variance and a list of user-specified variables. Note that in the case of clogithet it is the scale parameter which is a function of exp(Z*gamma), rather than the variance itself. The scale parameter is inversely related to the variance (see Hole, 2006, for details). The variables in Z must be constant within groups, i.e. they must be characteristics of the decision-maker rather than alternative attributes.

The data setup is the same as for clogit.

See logistic estimation commands for a list of related estimation commands.


+-------+ ----+ Model +------------------------------------------------------------

group(varname) is required; it specifies a numeric identifier variable for the matched groups.

het(varlist) is required; it specifies the independent variables in the variance function.

+-----------+ ----+ SE/Robust +--------------------------------------------------------

robust, cluster(varname); see estimation options.

opg; standard errors based on the outer product of gradient estimate of the covariance matrix.

+-----------+ ----+ Reporting +--------------------------------------------------------

lm; report Lagrange multiplier test for heteroscedasticity (H0: gamma =0).

clogit; report (homoscedastic) conditional logit estimates.

+-------------+ ----+ Max options +------------------------------------------------------ maximize_options; technique(algorithm_spec), iterate(#), trace, gradient, showstep, hessian, tolerance(#), ltolerance(#) gtolerance(#), nrtolerance(#), from(init_specs), difficult; see maximize. Note that technique(bhhh) is not allowed.


. webuse travel . gen aasc = (mode == 1) . gen tasc = (mode == 2) . gen basc = (mode == 3) . clogithet choice aasc tasc basc termtime travelcost, group(id) het(partysize) . clogithet choice aasc tasc basc termtime travelcost, group(id) het(partysize) robust lm


DeShazo, J.R., Fermo, G., 2002. Designing choice sets for stated preference methods: the effects of complexity on choice consistency. Journal of Environmental Economics and Management 44, 123-143.

Hensher, D., Louviere, J., Swait, J., 1999. Combining sources of preference data. Journal of Econometrics 89, 197-221.

Hole, A.R., 2006. Small-sample properties of tests for heteroscedasticity in the conditional logit model. Economics Bulletin 3, 1-14. Available at http://economicsbulletin.vanderbilt.edu/2006/volume3/EB-06C20063A.pdf


This command was written by Arne Risa Hole (a.r.hole@sheffield.ac.uk), Department of Economics, University of Sheffield. Comments and suggestions are welcome.

Also see

Manual: [R] clogit

Online: clogit