IDEAS home Printed from https://ideas.repec.org/a/eee/eejocm/v26y2018icp64-79.html
   My bibliography  Save this article

Facilitating an expectation-maximization (EM) algorithm to solve an integrated choice and latent variable (ICLV) model with fully correlated latent variables

Author

Listed:
  • Chae, Dasol
  • Jung, Jaeyoung
  • Sohn, Keemin

Abstract

It is well known that estimating the parameters of an integrated choice and latent variable (ICLV) model is not a trivial undertaking. The log-likelihood of an ICLV model cannot be evaluated analytically, and can only be evaluated by a simulation that requires large numbers of sample draws. While conducting simulation-based model estimations, researchers often encounter an estimation failure. Sohn (2017) suggests a novel estimation method to circumvent the problem by using an expectation-maximization algorithm (EM). However, a drawback of this method continues to be the requirement of a huge amount of computer memory to deal with an augmented covariance matrix. In the present study, this problem was overcome by connecting each latent variable in a structural equation to all individual specific variables. This restriction did not hamper the utility of an ICLV model during empirical experimentation. The main contribution of this study is to introduce a simple method devised to solve large-scale ICLV models.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:eejocm:v:26:y:2018:i:c:p:64-79
    DOI: 10.1016/j.jocm.2017.08.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1755534517300180
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jocm.2017.08.001?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. 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.
    2. Keemin Sohn, 2017. "An Expectation-Maximization Algorithm to Estimate the Integrated Choice and Latent Variable Model," Transportation Science, INFORMS, vol. 51(3), pages 946-967, August.
    3. Cherchi, Elisabetta & Guevara, Cristian Angelo, 2012. "A Monte Carlo experiment to analyze the curse of dimensionality in estimating random coefficients models with a full variance–covariance matrix," Transportation Research Part B: Methodological, Elsevier, vol. 46(2), pages 321-332.
    4. Bhat, Chandra R., 2003. "Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 837-855, November.
    5. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    6. Bhat, Chandra R. & Dubey, Subodh K., 2014. "A new estimation approach to integrate latent psychological constructs in choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 68-85.
    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. Kim, Seheon & Rasouli, Soora, 2022. "The influence of latent lifestyle on acceptance of Mobility-as-a-Service (MaaS): A hierarchical latent variable and latent class approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 304-319.

    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. Kim, Seheon & Rasouli, Soora, 2022. "The influence of latent lifestyle on acceptance of Mobility-as-a-Service (MaaS): A hierarchical latent variable and latent class approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 304-319.
    2. Keemin Sohn, 2017. "An Expectation-Maximization Algorithm to Estimate the Integrated Choice and Latent Variable Model," Transportation Science, INFORMS, vol. 51(3), pages 946-967, August.
    3. 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.
    4. Stefan Hochguertel & Henry Ohlsson, 2009. "Compensatory inter vivos gifts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(6), pages 993-1023.
    5. 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.
    6. Bouscasse, H., 2018. "Integrated choice and latent variable models: A literature review on mode choice," Working Papers 2018-07, Grenoble Applied Economics Laboratory (GAEL).
    7. Yonezawa, Koichi & Richards, Timothy J., 2016. "Competitive Package Size Decisions," Journal of Retailing, Elsevier, vol. 92(4), pages 445-469.
    8. Hess, Stephane & Train, Kenneth E., 2011. "Recovery of inter- and intra-personal heterogeneity using mixed logit models," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 973-990, August.
    9. repec:dgr:uvatin:20070074 is not listed on IDEAS
    10. Guevara, C. Angelo, 2015. "Critical assessment of five methods to correct for endogeneity in discrete-choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 82(C), pages 240-254.
    11. Bansal, Prateek & Daziano, Ricardo A & Guerra, Erick, 2018. "Minorization-Maximization (MM) algorithms for semiparametric logit models: Bottlenecks, extensions, and comparisons," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 17-40.
    12. Hélène Bouscasse, 2018. "Integrated choice and latent variable models: A literature review on mode choice," Working Papers hal-01795630, HAL.
    13. Stefan Hochguertel & Henry Ohlsson, 2009. "Compensatory inter vivos gifts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(6), pages 993-1023.
    14. 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.
    15. Bansal, Prateek & Krueger, Rico & Bierlaire, Michel & Daziano, Ricardo A. & Rashidi, Taha H., 2020. "Bayesian estimation of mixed multinomial logit models: Advances and simulation-based evaluations," Transportation Research Part B: Methodological, Elsevier, vol. 131(C), pages 124-142.
    16. Sandorf, Erlend Dancke & Aanesen, Margrethe & Navrud, Ståle, 2016. "Valuing unfamiliar and complex environmental goods: A comparison of valuation workshops and internet panel surveys with videos," Ecological Economics, Elsevier, vol. 129(C), pages 50-61.
    17. Prateek Bansal & Rico Krueger & Michel Bierlaire & Ricardo A. Daziano & Taha H. Rashidi, 2019. "Bayesian Estimation of Mixed Multinomial Logit Models: Advances and Simulation-Based Evaluations," Papers 1904.03647, arXiv.org, revised Dec 2019.
    18. Averi Chakrabarti & Karen A Grépin & Stéphane Helleringer, 2019. "The impact of supplementary immunization activities on routine vaccination coverage: An instrumental variable analysis in five low-income countries," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-11, February.
    19. Harold Alderman & John Hoddinott & Bill Kinsey, 2006. "Long term consequences of early childhood malnutrition," Oxford Economic Papers, Oxford University Press, vol. 58(3), pages 450-474, July.
    20. Huh, Yesol & Kim, You Suk, 2023. "Cheapest-to-deliver pricing, optimal MBS securitization, and welfare implications," Journal of Financial Economics, Elsevier, vol. 150(1), pages 68-93.
    21. Fan Li & Prashant Loyalka & Hongmei Yi & Yaojiang Shi & Natalie Johnson & Scott Rozelle, 2016. "Ability tracking and social capital in China's rural secondary school system," Working Papers of LICOS - Centre for Institutions and Economic Performance 544339, KU Leuven, Faculty of Economics and Business (FEB), LICOS - Centre for Institutions and Economic Performance.

    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:eejocm:v:26:y:2018:i:c:p:64-79. 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.journals.elsevier.com/journal-of-choice-modelling .

    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.