A multi-level cross-classified model for discrete response variables
In many spatial analysis contexts, the variable of interest is discrete and there is spatial clustering of observations. This paper formulates a model that accommodates clustering along more than one dimension in the context of a discrete response variable. For example, in a travel mode choice context, individuals are clustered by both the home zone in which they live as well as by their work locations. The model formulation takes the form of a mixed logit structure and is estimated by maximum likelihood using a combination of Gaussian quadrature and quasi-Monte Carlo simulation techniques. An application to travel mode choice suggests that ignoring the spatial context in which individuals make mode choice decisions can lead to an inferior data fit as well as provide inconsistent evaluations of transportation policy measures.
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Volume (Year): 34 (2000)
Issue (Month): 7 (September)
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References listed on IDEAS
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- Feenberg, Daniel & Skinner, Jonathan, 1994.
"The Risk and Duration of Catastrophic Health Care Expenditures,"
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- Daniel Feenberg & Jonathan Skinner, 1992. "The Risk and Duration of Catastrophic Health Care Expenditures," NBER Working Papers 4147, National Bureau of Economic Research, Inc.
- N Bullen & K Jones & C Duncan, 1997. "Modelling complexity: analysing between-individual and between-place variation -- a multilevel tutorial," Environment and Planning A, Pion Ltd, London, vol. 29(4), pages 585-609, April.
- Bhat, Chandra R., 1998. "Accommodating variations in responsiveness to level-of-service measures in travel mode choice modeling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(7), pages 495-507, September.
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