IDEAS home Printed from https://ideas.repec.org/a/kap/transp/v40y2013i3p647-670.html
   My bibliography  Save this article

Covariance, identification, and finite-sample performance of the MSL and Bayes estimators of a logit model with latent attributes

Author

Listed:
  • Ricardo Daziano
  • Denis Bolduc

Abstract

In this paper we discuss the specification, covariance structure, estimation, identification, and point-estimate analysis of a logit model with endogenous latent attributes that avoids problems of inconsistency. We show first that the total error term induced by the stochastic latent attributes is heteroskedastic and nonindependent. In addition, we show that the exact identification conditions support the two-stage analysis found in much current work. Second, we set up a Monte Carlo experiment where we compare the finite-sample performance of the point estimates of two alternative methods of estimation, namely frequentist full information maximum simulated likelihood and Bayesian Metropolis Hastings-within-Gibbs sampling. The Monte Carlo study represents a virtual case of travel mode choice. Even though the two estimation methods we analyze are based on different philosophies, both the frequentist and Bayesian methods provide estimators that are asymptotically equivalent. Our results show that both estimators are feasible and offer comparable results with a large enough sample size. However, the Bayesian point estimates outperform maximum likelihood in terms of accuracy, statistical significance, and efficiency when the sample size is low. Copyright Springer Science+Business Media, LLC. 2013

Suggested Citation

  • Ricardo Daziano & Denis Bolduc, 2013. "Covariance, identification, and finite-sample performance of the MSL and Bayes estimators of a logit model with latent attributes," Transportation, Springer, vol. 40(3), pages 647-670, May.
  • Handle: RePEc:kap:transp:v:40:y:2013:i:3:p:647-670
    DOI: 10.1007/s11116-012-9434-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11116-012-9434-5
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11116-012-9434-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Vredin Johansson, Maria & Heldt, Tobias & Johansson, Per, 2006. "The effects of attitudes and personality traits on mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(6), pages 507-525, July.
    2. Joel Huber and Kenneth Train., 2000. "On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths," Economics Working Papers E00-289, University of California at Berkeley.
    3. 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.
    4. Gourieroux,Christian & Monfort,Alain, 1995. "Statistics and Econometric Models," Cambridge Books, Cambridge University Press, number 9780521471626, October.
    5. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    6. Brownstone, David & Train, Kenneth, 1998. "Forecasting new product penetration with flexible substitution patterns," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 109-129, November.
    7. Daniel McFadden, 2001. "Economic Choices," American Economic Review, American Economic Association, vol. 91(3), pages 351-378, June.
    8. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
    9. Gibson, Fiona L. & Burton, Michael P., 2009. "Biased estimates in discrete choice models: the appropriate inclusion of psychometric data into the valuation of recycled wastewater," 2009 Conference (53rd), February 11-13, 2009, Cairns, Australia 47943, Australian Agricultural and Resource Economics Society.
    10. Daniel McFadden, 1986. "The Choice Theory Approach to Market Research," Marketing Science, INFORMS, vol. 5(4), pages 275-297.
    11. Ben-Akiva, Moshe & McFadden, Daniel & Train, Kenneth & Börsch-Supan, Axel, 2002. "Hybrid Choice Models: Progress and Challenges," Sonderforschungsbereich 504 Publications 02-29, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Vij, Akshay & Walker, Joan L., 2016. "How, when and why integrated choice and latent variable models are latently useful," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 192-217.
    2. Akshay Vij & Joan L. Walker, 2014. "Hybrid choice models: the identification problem," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 22, pages 519-564, Edward Elgar Publishing.
    3. Bouscasse, H., 2018. "Integrated choice and latent variable models: A literature review on mode choice," Working Papers 2018-07, Grenoble Applied Economics Laboratory (GAEL).
    4. Xuemei Fu & Zhicai Juan, 2017. "Estimation of multinomial probit-kernel integrated choice and latent variable model: comparison on one sequential and two simultaneous approaches," Transportation, Springer, vol. 44(1), pages 91-116, January.
    5. Schmid, Basil & Axhausen, Kay W., 2019. "In-store or online shopping of search and experience goods: A hybrid choice approach," Journal of choice modelling, Elsevier, vol. 31(C), pages 156-180.
    6. Daziano, Ricardo A. & Chiew, Esther, 2012. "Electric vehicles rising from the dead: Data needs for forecasting consumer response toward sustainable energy sources in personal transportation," Energy Policy, Elsevier, vol. 51(C), pages 876-894.
    7. Marcel Paulssen & Dirk Temme & Akshay Vij & Joan Walker, 2014. "Values, attitudes and travel behavior: a hierarchical latent variable mixed logit model of travel mode choice," Transportation, Springer, vol. 41(4), pages 873-888, July.
    8. Chae, Dasol & Jung, Jaeyoung & Sohn, Keemin, 2018. "Facilitating an expectation-maximization (EM) algorithm to solve an integrated choice and latent variable (ICLV) model with fully correlated latent variables," Journal of choice modelling, Elsevier, vol. 26(C), pages 64-79.
    9. Daziano, Ricardo A., 2015. "Inference on mode preferences, vehicle purchases, and the energy paradox using a Bayesian structural choice model," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 1-26.
    10. Hélène Bouscasse, 2018. "Integrated choice and latent variable models: A literature review on mode choice," Working Papers hal-01795630, HAL.
    11. Rose, John M. & Hensher, David A., 2018. "User satisfaction with taxi and limousine services in the Melbourne metropolitan area," Journal of Transport Geography, Elsevier, vol. 70(C), pages 234-245.

    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. Haghani, Milad & Bliemer, Michiel C.J. & Hensher, David A., 2021. "The landscape of econometric discrete choice modelling research," Journal of choice modelling, Elsevier, vol. 40(C).
    2. Xuemei Fu & Zhicai Juan, 2017. "Estimation of multinomial probit-kernel integrated choice and latent variable model: comparison on one sequential and two simultaneous approaches," Transportation, Springer, vol. 44(1), pages 91-116, January.
    3. Joan L. Walker & Moshe Ben-Akiva, 2011. "Advances in Discrete Choice: Mixture Models," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 8, Edward Elgar Publishing.
    4. Sándor, Zsolt & Train, Kenneth, 2004. "Quasi-random simulation of discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 38(4), pages 313-327, May.
    5. Alexandros Dimitropoulos, 2014. "The Influence of Environmental Concerns on Drivers’ Preferences for Electric Cars," Tinbergen Institute Discussion Papers 14-128/VIII, Tinbergen Institute.
    6. van der Kroon, Bianca & Brouwer, Roy & van Beukering, Pieter J.H., 2014. "The impact of the household decision environment on fuel choice behavior," Energy Economics, Elsevier, vol. 44(C), pages 236-247.
    7. Aurélie Glerum & Lidija Stankovikj & Michaël Thémans & Michel Bierlaire, 2014. "Forecasting the Demand for Electric Vehicles: Accounting for Attitudes and Perceptions," Transportation Science, INFORMS, vol. 48(4), pages 483-499, November.
    8. Xiong, Yingge & Mannering, Fred L., 2013. "The heterogeneous effects of guardian supervision on adolescent driver-injury severities: A finite-mixture random-parameters approach," Transportation Research Part B: Methodological, Elsevier, vol. 49(C), pages 39-54.
    9. Jennifer Roberts & Gurleen Popli & Rosemary J. Harris, 2018. "Do environmental concerns affect commuting choices?: hybrid choice modelling with household survey data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(1), pages 299-320, January.
    10. Fabian Bastin & Cinzia Cirillo & Philippe L. Toint, 2010. "Estimating Nonparametric Random Utility Models with an Application to the Value of Time in Heterogeneous Populations," Transportation Science, INFORMS, vol. 44(4), pages 537-549, November.
    11. Hu, Wuyang & Adamowicz, Wiktor L. & Veeman, Michele M., 2005. "Bayesian Analysis of Consumer Choices with Taste, Context, Reference Point and Individual Scale Effects," 2005 Annual meeting, July 24-27, Providence, RI 19296, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    12. Victoria Prowse, 2012. "Modeling Employment Dynamics With State Dependence and Unobserved Heterogeneity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 411-431, April.
    13. 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.
    14. Katherine Silz Carson & Susan M. Chilton & W. George Hutchinson & Riccardo Scarpa, 2020. "Public resource allocation, strategic behavior, and status quo bias in choice experiments," Public Choice, Springer, vol. 185(1), pages 1-19, October.
    15. Brownstone, David, 2001. "Discrete Choice Modeling for Transportation," University of California Transportation Center, Working Papers qt29v7d1pk, University of California Transportation Center.
    16. Patil, Priyadarshan N. & Dubey, Subodh K. & Pinjari, Abdul R. & Cherchi, Elisabetta & Daziano, Ricardo & Bhat, Chandra R., 2017. "Simulation evaluation of emerging estimation techniques for multinomial probit models," Journal of choice modelling, Elsevier, vol. 23(C), pages 9-20.
    17. Heiss, Florian & Winschel, Viktor, 2008. "Likelihood approximation by numerical integration on sparse grids," Journal of Econometrics, Elsevier, vol. 144(1), pages 62-80, May.
    18. Daziano, Ricardo A. & Chiew, Esther, 2012. "Electric vehicles rising from the dead: Data needs for forecasting consumer response toward sustainable energy sources in personal transportation," Energy Policy, Elsevier, vol. 51(C), pages 876-894.
    19. Kennedy Otieno, Pambo, 2013. "Analysis of Consumer Awareness and Preferences for Fortified Sugar in Kenya," Research Theses 243455, Collaborative Masters Program in Agricultural and Applied Economics.
    20. Tran, Martino, 2012. "Technology-behavioural modelling of energy innovation diffusion in the UK," Applied Energy, Elsevier, vol. 95(C), pages 1-11.

    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:kap:transp:v:40:y:2013:i:3:p:647-670. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.