IDEAS home Printed from https://ideas.repec.org/p/fip/fedcwp/1219.html
   My bibliography  Save this paper

A tractable estimator for general mixed multinomial logit models

Author

Listed:
  • Jonathan James

Abstract

The mixed logit is a framework for incorporating unobserved heterogeneity in discrete choice models in a general way. These models are difficult to estimate because they result in a complicated incomplete data likelihood. This paper proposes a new approach for estimating mixed logit models. The estimator is easily implemented as iteratively re-weighted least squares: the well known solution for complete data likelihood logits. The main benefit of this approach is that it requires drastically fewer evaluations of the simulated likelihood function, making it significantly faster than conventional methods that rely on numerically approximating the gradient. The method is rooted in a generalized expectation and maximization (GEM) algorithm, so it is asymptotically consistent, efficient, and globally convergent.

Suggested Citation

  • Jonathan James, 2012. "A tractable estimator for general mixed multinomial logit models," Working Papers (Old Series) 1219, Federal Reserve Bank of Cleveland.
    • Handle: RePEc:fip:fedcwp:1219
    DOI: 10.26509/frbc-wp-201219
    as

    Download full text from publisher

    File URL: https://doi.org/10.26509/frbc-wp-201219
    File Function: Persistent Link
    Download Restriction: no
    File URL: https://libkey.io/10.26509/frbc-wp-201219?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
    ---><---

    References listed on IDEAS

    as
    1. Nielsen, Soren Feodor, 2000. "On simulated EM algorithms," Journal of Econometrics, Elsevier, vol. 96(2), pages 267-292, June.
    2. Peter Arcidiacono & John Bailey Jones, 2003. "Finite Mixture Distributions, Sequential Likelihood and the EM Algorithm," Econometrica, Econometric Society, vol. 71(3), pages 933-946, May.
    3. Dankmar Böhning & Bruce Lindsay, 1988. "Monotonicity of quadratic-approximation algorithms," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 40(4), pages 641-663, December.
    4. Dankmar Böhning, 1992. "Multinomial logistic regression algorithm," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 44(1), pages 197-200, March.
    5. Bernal, Raquel & Keane, Michael P., 2010. "Quasi-structural estimation of a model of childcare choices and child cognitive ability production," Journal of Econometrics, Elsevier, vol. 156(1), pages 164-189, May.
    6. Kuroda, Masahiro & Sakakihara, Michio, 2006. "Accelerating the convergence of the EM algorithm using the vector [epsilon] algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1549-1561, December.
    7. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    8. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    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. repec:fip:fedcwp:12-19 is not listed on IDEAS
    2. Tian, Guo-Liang & Tang, Man-Lai & Liu, Chunling, 2012. "Accelerating the quadratic lower-bound algorithm via optimizing the shrinkage parameter," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 255-265.
    3. Michael Keane & Nada Wasi, 2013. "Comparing Alternative Models Of Heterogeneity In Consumer Choice Behavior," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 1018-1045, September.
    4. Fabián Slonimczyk & Vladimir Gimpelson, 2015. "Informality and mobility," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 23(2), pages 299-341, April.
    5. 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.
    6. Ortega, David L. & Wang, H. Holly & Wu, Laping & Hong, Soo Jeong, 2015. "Retail channel and consumer demand for food quality in China," China Economic Review, Elsevier, vol. 36(C), pages 359-366.
    7. Pereira, Pedro & Ribeiro, Tiago, 2011. "The impact on broadband access to the Internet of the dual ownership of telephone and cable networks," International Journal of Industrial Organization, Elsevier, vol. 29(2), pages 283-293, March.
    8. Choi, Andy S., 2013. "Nonmarket values of major resources in the Korean DMZ areas: A test of distance decay," Ecological Economics, Elsevier, vol. 88(C), pages 97-107.
    9. Doherty, Edel & Campbell, Danny, 2011. "Demand for improved food safety and quality: a cross-regional comparison," 85th Annual Conference, April 18-20, 2011, Warwick University, Coventry, UK 108791, Agricultural Economics Society.
    10. Abdurrahman B. Aydemir & Erkan Duman, 2021. "Migrant Networks and Destination Choice: Evidence from Moves across Turkish Provinces," Koç University-TUSIAD Economic Research Forum Working Papers 2109, Koc University-TUSIAD Economic Research Forum.
    11. Fosgerau, Mogens & Bierlaire, Michel, 2007. "A practical test for the choice of mixing distribution in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 41(7), pages 784-794, August.
    12. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    13. Veneziani, Mario & Sckokai, Paolo & Moro, Daniele, 2012. "Consumers’ willingness to pay for a functional food," 2012 First Congress, June 4-5, 2012, Trento, Italy 124101, Italian Association of Agricultural and Applied Economics (AIEAA).
    14. Kesternich, Iris & Heiss, Florian & McFadden, Daniel & Winter, Joachim, 2013. "Suit the action to the word, the word to the action: Hypothetical choices and real decisions in Medicare Part D," Journal of Health Economics, Elsevier, vol. 32(6), pages 1313-1324.
    15. Jianhua Wang & Jiaye Ge & Yuting Ma, 2018. "Urban Chinese Consumers’ Willingness to Pay for Pork with Certified Labels: A Discrete Choice Experiment," Sustainability, MDPI, vol. 10(3), pages 1-14, February.
    16. Qian, Lixian & Grisolía, Jose M. & Soopramanien, Didier, 2019. "The impact of service and government-policy attributes on consumer preferences for electric vehicles in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 122(C), pages 70-84.
    17. Scaccia, Luisa & Marcucci, Edoardo & Gatta, Valerio, 2023. "Prediction and confidence intervals of willingness-to-pay for mixed logit models," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 54-78.
    18. Hyunjoo Lee & Misuk Lee & Sesil Lim, 2018. "Do Consumers Care about the Energy Efficiency of Buildings? Understanding Residential Choice Based on Energy Performance Certificates," Sustainability, MDPI, vol. 10(11), pages 1-18, November.
    19. Shin, Jungwoo & Hwang, Won-Sik, 2017. "Consumer preference and willingness to pay for a renewable fuel standard (RFS) policy: Focusing on ex-ante market analysis and segmentation," Energy Policy, Elsevier, vol. 106(C), pages 32-40.
    20. Don Fullerton & Li Gan & Miwa Hattori, 2015. "A model to evaluate vehicle emission incentive policies in Japan," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 17(1), pages 79-108, January.
    21. Liu, Ruifeng & ,, 2021. "What We Can Learn from the Interactions of Food Traceable Attributes? a Case Study of Fuji Apple in China," 2021 Conference, August 17-31, 2021, Virtual 315916, International Association of Agricultural Economists.

    More about this item

    Keywords

    Econometrics; Econometric models;

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:fip:fedcwp:1219. 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: 4D Library (email available below). General contact details of provider: https://edirc.repec.org/data/frbclus.html .

    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.