@article{Domanski:49413,
      recid = {49413},
      author = {Domanski, Adam},
      title = {Estimating Mixed Logit Recreation Demand Models With Large  Choice Sets},
      address = {2009},
      number = {319-2016-9804},
      pages = {46},
      year = {2009},
      abstract = {Discrete choice models are widely used in studies of  recreation demand.  They have proven valuable when modeling  situations where decision makers face large choice sets and  site substitution is important.  However, when the choice  set faced by the individual becomes very large (on the  order of hundreds or thousands of alternatives),  computational limitations make estimation with the full  choice set intractable.  Sampling of alternatives in a  conditional logit framework is an effective method to limit  computational burdens while still producing consistent  estimates.  This method is allowed by the existence of the  independence of irrelevant alternatives (IIA) assumption.   More advanced mixed logit models account for unobserved  preference heterogeneity and overcome the behavioral  limitations of the IIA assumption, however in doing so,  prohibit sampling of alternatives.  A method is developed  where a latent class (finite mixture) model is estimated  via the expectations-maximization algorithm and in doing  so, allows consistent sampling of alternatives in a mixed  logit model.  The method is tested and applied to a  recreational demand Wisconsin fishing survey.},
      url = {http://ageconsearch.umn.edu/record/49413},
      doi = {https://doi.org/10.22004/ag.econ.49413},
}