IDEAS home Printed from https://ideas.repec.org/a/eee/transb/v31y1997i1p11-21.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0191-2615(96)00018-5
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hausman, Jerry & McFadden, Daniel, 1984. "Specification Tests for the Multinomial Logit Model," Econometrica, Econometric Society, vol. 52(5), pages 1219-1240, September.
    2. Borsch-Supan, Axel & Hajivassiliou, Vassilis A., 1993. "Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models," Journal of Econometrics, Elsevier, vol. 58(3), pages 347-368, August.
    3. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 225-238.
    4. Horowitz, Joel L., 1991. "Reconsidering the multinomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 25(6), pages 433-438, December.
    5. Brownstone, David & Small, Kenneth A, 1989. "Efficient Estimation of Nested Logit Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(1), pages 67-74, January.
    6. Jain, Dipak C & Vilcassim, Naufel J & Chintagunta, Pradeep K, 1994. "A Random-Coefficients Logit Brand-Choice Model Applied to Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 317-328, July.
    7. Bhat, Chandra R., 1995. "A heteroscedastic extreme value model of intercity travel mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 29(6), pages 471-483, December.
    8. Manski, Charles F & Lerman, Steven R, 1977. "The Estimation of Choice Probabilities from Choice Based Samples," Econometrica, Econometric Society, vol. 45(8), pages 1977-1988, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Baltas, George & Doyle, Peter, 2001. "Random utility models in marketing research: a survey," Journal of Business Research, Elsevier, vol. 51(2), pages 115-125, February.
    2. Koppelman, Frank S. & Wen, Chieh-Hua, 2000. "The paired combinatorial logit model: properties, estimation and application," Transportation Research Part B: Methodological, Elsevier, vol. 34(2), pages 75-89, February.
    3. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    4. Daniel McFadden, 2001. "Economic Choices," American Economic Review, American Economic Association, vol. 91(3), pages 351-378, June.
    5. Soda, Giuseppe & Zaheer, Akbar & Sun, Xiaoming & Cui, Wentian, 2021. "Brokerage evolution in innovation contexts: Formal structure, network neighborhoods and knowledge," Research Policy, Elsevier, vol. 50(10).
    6. Ribar, David C., 2004. "What Do Social Scientists Know About the Benefits of Marriage? A Review of Quantitative Methodologies," IZA Discussion Papers 998, Institute of Labor Economics (IZA).
    7. Hajivassiliou, Vassilis, 2019. "Estimation and specification testing of panel data models with non-ignorable persistent heterogeneity, contemporaneous and intertemporal simultaneity and observable and unobservable dynamics," LSE Research Online Documents on Economics 102843, London School of Economics and Political Science, LSE Library.
    8. Breitung, Jörg & Lechner, Michael, 1998. "Alternative GMM methods for nonlinear panel data models," SFB 373 Discussion Papers 1998,81, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    9. Chidlow, Agnieszka & Holmström-Lind, Christine & Holm, Ulf & Tallman, Steve, 2015. "Do I stay or do I go? Sub-national drivers for post-entry subsidiary development," International Business Review, Elsevier, vol. 24(2), pages 266-275.
    10. Shakeeb Khan & Fu Ouyang & Elie Tamer, 2020. "Inference on Semiparametric Multinomial Response Models," Discussion Papers Series 627, School of Economics, University of Queensland, Australia.
    11. Hans G. Bloemen, 2002. "The relation between wealth and labour market transitions: an empirical study for the Netherlands," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(3), pages 249-268.
    12. Lee, Lung-Fei, 1997. "Simulated maximum likelihood estimation of dynamic discrete choice statistical models some Monte Carlo results," Journal of Econometrics, Elsevier, vol. 82(1), pages 1-35.
    13. Choo, Sangho & Mokhtarian, Patricia L., 2004. "What type of vehicle do people drive? The role of attitude and lifestyle in influencing vehicle type choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(3), pages 201-222, March.
    14. Chidlow, Agnieszka & Salciuviene, Laura & Young, Stephen, 2009. "Regional determinants of inward FDI distribution in Poland," International Business Review, Elsevier, vol. 18(2), pages 119-133, April.
    15. Poirier, Dale J., 1996. "A Bayesian analysis of nested logit models," Journal of Econometrics, Elsevier, vol. 75(1), pages 163-181, November.
    16. Joachim Grammig & Reinhard Hujer & Michael Scheidler, 2005. "Discrete choice modelling in airline network management," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 467-486, May.
    17. GRAMMIG, Joachim & HUJER, Reinhard & SCHEIDLER, Michael, 2001. "The econometrics of airline network management," LIDAM Discussion Papers CORE 2001055, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    18. Can, Vo Van, 2013. "Estimation of travel mode choice for domestic tourists to Nha Trang using the multinomial probit model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 149-159.
    19. 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.
    20. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transb:v:31:y:1997:i:1:p:11-21. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/548/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.