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Covariance heterogeneity in nested logit models: Econometric structure and application to intercity travel

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  • Bhat, Chandra R.

Abstract

The nested logit model relaxes the 'independence of irrelevant alternatives' (IIA) property of the multinomial logit model by grouping alternatives based on their degree of substitution. Alternatives in a nest exhibit an identical degree of increased sensitivity relative to alternatives not in the nest. An assumption maintained in the nested logit is that the degree of sensitivity among nested alternatives is invariant across agents making the choice. This assumption might be untenable in many situations. In this paper, we propose an extension of the nested logit model to allow heterogeneity (across agents) in the covariance among nested alternatives based on observed agent characteristics. We label this model as the COVNL model. The multinomial logit, the nested logit, and the COVNL model are estimated to examine the impact of improved rail service on weekday, business travel in the Toronto-Montreal corridor. The empirical results show that not accounting for covariance heterogeneity in the nested logit formulation leads to a statistically inferior data fit and also to biased model estimates of the effects of level-of-service variables.

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  • Bhat, Chandra R., 1997. "Covariance heterogeneity in nested logit models: Econometric structure and application to intercity travel," Transportation Research Part B: Methodological, Elsevier, vol. 31(1), pages 11-21, February.
  • Handle: RePEc:eee:transb:v:31:y:1997:i:1:p:11-21
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    Cited by:

    1. Michael Scheidler & Reinhard Hujer & Joachim Grammig, 2005. "Discrete choice modelling in airline network management," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 467-486.
    2. F Alpizar & F Carlsson & P Martinsson, 2003. "Using Choice Experiments for Non-Market Valuation," Economic Issues Journal Articles, Economic Issues, vol. 8(1), pages 83-110, March.
    3. Fosgerau, M. & Bierlaire, M., 2009. "Discrete choice models with multiplicative error terms," Transportation Research Part B: Methodological, Elsevier, vol. 43(5), pages 494-505, June.
    4. Peter Davis & Pasquale Schiraldi, 2014. "The flexible coefficient multinomial logit (FC-MNL) model of demand for differentiated products," RAND Journal of Economics, RAND Corporation, vol. 45(1), pages 32-63, March.
    5. Behrens, Christiaan & Pels, Eric, 2012. "Intermodal competition in the London–Paris passenger market: High-Speed Rail and air transport," Journal of Urban Economics, Elsevier, vol. 71(3), pages 278-288.
    6. Su, Qing & Zhou, Liren, 2012. "Parking management, financial subsidies to alternatives to drive alone and commute mode choices in Seattle," Regional Science and Urban Economics, Elsevier, vol. 42(1-2), pages 88-97.
    7. Wen, Chieh-Hua & Koppelman, Frank S., 2001. "The generalized nested logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 627-641, August.
    8. Flügel, Stefan & Halse, Askill H. & Ortúzar, Juan de Dios & Rizzi, Luis I., 2015. "Methodological challenges in modelling the choice of mode for a new travel alternative using binary stated choice data – The case of high speed rail in Norway," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 438-451.
    9. Anastassiadis, Friederike & Mußhoff, Oliver, 2013. "Evaluating the role of financial flexibility in farmers' investment decisions using latent class analysis," 87th Annual Conference, April 8-10, 2013, Warwick University, Coventry, UK 158707, Agricultural Economics Society.
    10. Kim, Sooil & Haab, Timothy C., 2005. "Generalized Estimation Methods for Non-i.i.d. Binary Data: An Application to Dichotomous Choice Contingent Valuation," 2005 Annual meeting, July 24-27, Providence, RI 19138, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    11. Marcucci, Edoardo & Gatta, Valerio, 2012. "Dissecting preference heterogeneity in consumer stated choices," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 331-339.
    12. Li, Baibing, 2011. "The multinomial logit model revisited: A semi-parametric approach in discrete choice analysis," Transportation Research Part B: Methodological, Elsevier, vol. 45(3), pages 461-473, March.
    13. Sergio Colombo & Nick Hanley & Jordan Louviere, 2009. "Modeling preference heterogeneity in stated choice data: an analysis for public goods generated by agriculture," Agricultural Economics, International Association of Agricultural Economists, vol. 40(3), pages 307-322, May.
    14. GRAMMIG, Joachim & HUJER, Reinhard & SCHEIDLER, Michael, 2001. "The econometrics of airline network management," CORE Discussion Papers 2001055, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    15. Christiaan Behrens & Eric Pels, 2009. "Intermodal Competition in The London-Paris Passenger Market: High-Speed Rail and Air Transport," Tinbergen Institute Discussion Papers 09-051/3, Tinbergen Institute.
    16. Fosgerau, Mogens & Bierlaire, Michel, 2007. "Circumventing the problem of the scale: discrete choice models with multiplicative error terms," MPRA Paper 3901, University Library of Munich, Germany.
    17. Man Li & Alessandro Pinto & John M. Ulimwengu & Liangzhi You & Richard D. Robertson, 2015. "Impacts of Road Expansion on Deforestation and Biological Carbon Loss in the Democratic Republic of Congo," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 60(3), pages 433-469, March.

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