IDEAS home Printed from https://ideas.repec.org/a/taf/apeclt/v12y2005i12p741-744.html
   My bibliography  Save this article

Logit models: smallest versus largest extreme value error distributions

Author

Listed:
  • Wuyang Hu

Abstract

The general term 'type I extreme value distribution' underlying the logit model is not fully precise. Through a case study, this study compares two models based on the distribution - one with the smallest specification and one with the largest. Results show these two models are different.

Suggested Citation

  • Wuyang Hu, 2005. "Logit models: smallest versus largest extreme value error distributions," Applied Economics Letters, Taylor & Francis Journals, vol. 12(12), pages 741-744.
  • Handle: RePEc:taf:apeclt:v:12:y:2005:i:12:p:741-744
    DOI: 10.1080/13504850500192457
    as

    Download full text from publisher

    File URL: http://www.informaworld.com/openurl?genre=article&doi=10.1080/13504850500192457&magic=repec&7C&7C8674ECAB8BB840C6AD35DC6213A474B5
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/13504850500192457?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. 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.
    2. Lee, Lung-Fei, 1992. "On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Response Models," Econometric Theory, Cambridge University Press, vol. 8(4), pages 518-552, December.
    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. Hu, Wuyang, 2007. "A Choice Model with Systematic Structures in Decision Weights," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 32(3), pages 1-14, December.
    2. Junyi Shen, 2009. "A choice experiment approachin evaluating publictransportation projects," Applied Economics Letters, Taylor & Francis Journals, vol. 16(6), pages 557-561.
    3. Richard T. Carson & Derrick H. Sun & Yixiao Sun, 2024. "Random Utility Models with Skewed Random Components: the Smallest versus Largest Extreme Value Distribution," Papers 2405.08222, arXiv.org, revised May 2024.
    4. Hu, Wuyang, 2006. "Effects of Endogenous Task Complexity and the Endowed Bundle on Stated Choice," 2006 Annual meeting, July 23-26, Long Beach, CA 21437, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    5. Wuyang Hu, 2008. "Modeling discrete choices with augmented perception hurdles," Agricultural Economics, International Association of Agricultural Economists, vol. 39(2), pages 257-267, September.
    6. Wuyang Hu & Linda J. Cox & Quincy A. Edwards, 2007. "The market potential for gift baskets of Hawaiian food products in China," Agribusiness, John Wiley & Sons, Ltd., vol. 23(4), pages 553-565.

    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. 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.
    2. Mayer, Alexander & Wied, Dominik, 2023. "Estimation and inference in factor copula models with exogenous covariates," Journal of Econometrics, Elsevier, vol. 235(2), pages 1500-1521.
    3. Jason R. Blevins, 2015. "Structural Estimation Of Sequential Games Of Complete Information," Economic Inquiry, Western Economic Association International, vol. 53(2), pages 791-811, April.
    4. Kristensen, Dennis & Salanié, Bernard, 2017. "Higher-order properties of approximate estimators," Journal of Econometrics, Elsevier, vol. 198(2), pages 189-208.
    5. Chihwa Kao & Lung-fei Lee & Mark M. Pitt, 2001. "Simulated Maximum Likelihood Estimation of the Linear Expenditure System with Binding Non-Negativity Constraints," Annals of Economics and Finance, Society for AEF, vol. 2(1), pages 215-235, May.
    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. Yang, Yudi & Fan, Yueyue & Royset, Johannes O., 2019. "Estimating probability distributions of travel demand on a congested network," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 265-286.
    8. Michael P. Keane & Robert M. Sauer, 2010. "A Computationally Practical Simulation Estimation Algorithm For Dynamic Panel Data Models With Unobserved Endogenous State Variables," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 51(4), pages 925-958, November.
    9. Vassilis A. Hajivassiliou, 1991. "Simulation Estimation Methods for Limited Dependent Variable Models," Cowles Foundation Discussion Papers 1007, Cowles Foundation for Research in Economics, Yale University.
    10. Xiaodong Gong & Arthur van Soest, 2002. "Family Structure and Female Labor Supply in Mexico City," Journal of Human Resources, University of Wisconsin Press, vol. 37(1), pages 163-191.
    11. Michael Lechner & Stefan Lollivier & Thierry Magnac, 2005. "Parametric Binary Choice Models," University of St. Gallen Department of Economics working paper series 2005 2005-23, Department of Economics, University of St. Gallen.
    12. Hahn, Jinyong & Liu, Xueyuan, 2022. "Jackknife bias reduction for simulated maximum likelihood estimator of discrete choice models," Economics Letters, Elsevier, vol. 219(C).
    13. Anindya Biswas & Biswajit Mandal, 2016. "Estimating Preference Parameters From Stock Returns Using Simulated Method Of Moments," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 1-13, March.
    14. Geweke, John F. & Keane, Michael P. & Runkle, David E., 1997. "Statistical inference in the multinomial multiperiod probit model," Journal of Econometrics, Elsevier, vol. 80(1), pages 125-165, September.
    15. Mario Martinoli & Raffaello Seri & Fulvio Corsi, 2024. "Generalized Optimization Algorithms for Complex Models," LEM Papers Series 2024/18, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    16. Paul Gertler & Roland Sturm & Bruce Davidson, 1994. "Information and the Demand for Supplemental Medicare Insurance," NBER Working Papers 4700, National Bureau of Economic Research, Inc.
    17. Dennis Kristensen & Bernard Salanié, 2010. "Higher Order Improvements for Approximate Estimators," CAM Working Papers 2010-04, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.
    18. Brownstone, David, 2001. "Discrete Choice Modeling for Transportation," University of California Transportation Center, Working Papers qt29v7d1pk, University of California Transportation Center.
    19. Rinus Haaijer & Michel Wedel & Marco Vriens & Tom Wansbeek, 1998. "Utility Covariances and Context Effects in Conjoint MNP Models," Marketing Science, INFORMS, vol. 17(3), pages 236-252.
    20. Lee, Lung-Fei, 1997. "A simulated likelihood estimator for qualitative response models with sufficient statistics," Economics Letters, Elsevier, vol. 57(1), pages 23-32, November.

    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:taf:apeclt:v:12:y:2005:i:12:p:741-744. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEL20 .

    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.