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Accounting for scale heterogeneity within and between pooled data sources


  • Hensher, David A.


There is growing interest in incorporating both preference heterogeneity and scale heterogeneity in choice models, as a way of capturing an increasing number of sources of utility amongst a set of alternatives. The extension of mixed logit to incorporate scale heterogeneity in a generalised mixed logit (GMXL) model provides a way to accommodate these sources of influence, observed and unobserved. The small but growing number of applications of the GMXL model have parameterized scale heterogeneity as a single estimate; however it is often the case that analysts pool data from more than one source, be it revealed preference (RP) and stated preference (SP) sources, or multiple SP sources, inducing the potential for differences in the scale factor between the data sources. Existing practice has developed ways of accommodating scale differences between data sources by adopting a scale homogeneity assumption within each data source (e.g., the nested logit trick) that varies between data sources. This paper extends the state of the art by incorporating data-source specific scale differences in scale heterogeneity setting across pooled RP and SP data set. An example of choice amongst RP and SP transport modes (including two ‘new’ SP modes) is used to obtain values of travel time savings that vary significantly between a model that accounts for scale heterogeneity differences within pooled RP and SP data, and the other where differences in scale heterogeneity is also accommodated between RP and SP data.

Suggested Citation

  • Hensher, David A., 2012. "Accounting for scale heterogeneity within and between pooled data sources," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(3), pages 480-486.
  • Handle: RePEc:eee:transa:v:46:y:2012:i:3:p:480-486
    DOI: 10.1016/j.tra.2011.11.007

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    References listed on IDEAS

    1. Denzil G. Fiebig & Michael P. Keane & Jordan Louviere & Nada Wasi, 2010. "The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity," Marketing Science, INFORMS, vol. 29(3), pages 393-421, 05-06.
    2. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    3. Bhat, Chandra R. & Castelar, Saul, 2002. "A unified mixed logit framework for modeling revealed and stated preferences: formulation and application to congestion pricing analysis in the San Francisco Bay area," Transportation Research Part B: Methodological, Elsevier, vol. 36(7), pages 593-616, August.
    4. Brownstone, David & Bunch, David S. & Train, Kenneth, 2000. "Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 34(5), pages 315-338, June.
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    Cited by:

    1. Kragt, Marit Ellen, 2013. "Comparing models of unobserved heterogeneity in environmental choice experiments," Working Papers 144447, University of Western Australia, School of Agricultural and Resource Economics.
    2. Ratcliffe, Julie & Huynh, Elisabeth & Chen, Gang & Stevens, Katherine & Swait, Joffre & Brazier, John & Sawyer, Michael & Roberts, Rachel & Flynn, Terry, 2016. "Valuing the Child Health Utility 9D: Using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm," Social Science & Medicine, Elsevier, vol. 157(C), pages 48-59.
    3. John C. Whitehead & Daniel K. Lew, 2016. "Estimating Recreation Benefits through Joint Estimation of Revealed and Stated Preference Discrete Choice Data," Working Papers 16-22, Department of Economics, Appalachian State University.
    4. Richard Andrew Iles, 2013. "Demand for primary healthcare in rural north India," 2013 Papers pil50, Job Market Papers.
    5. Haghani, Milad & Sarvi, Majid & Shahhoseini, Zahra, 2015. "Accommodating taste heterogeneity and desired substitution pattern in exit choices of pedestrian crowd evacuees using a mixed nested logit model," Journal of choice modelling, Elsevier, vol. 16(C), pages 58-68.
    6. Javier Anta & José B. Pérez-López & Ana Martínez-Pardo & Margarita Novales & Alfonso Orro, 2016. "Influence of the weather on mode choice in corridors with time-varying congestion: a mixed data study," Transportation, Springer, vol. 43(2), pages 337-355, March.


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