IDEAS home Printed from https://ideas.repec.org/p/msh/ebswps/2002-7.html
   My bibliography  Save this paper

The DOGEV Model

Author

Listed:
  • Tim R.L. Fry
  • Mark N. Harris

Abstract

At present, there appears to be no qualitative dependent model that can simultaneously account for data sets in which the variable of interest is potentially ordered but also has strong heterogeneity of the observed outcomes. This heterogeneity of particular outcomes, inherently attracts individuals to them, in addition to that determined by the individual's observed characteristics. An example of such unobserved heterogeneity would be brand-loyalty (or "captivity") in a model of consumer choice. Such heterogeneity of the outcomes, may well result in a pronounced multi-modal distribution of the variable of interest. This paper introduces the Dogit Ordered Generalized Extreme Value (DOGEV) model, which does account for both ordering and captivity (and/or multiple modes) in the data. In the spirit of Manski (1977), the DOGEV model combines a model for choice set generation with the Ordered Generalized Extreme Value model. We illustrate the model using three different empirical examples: a model of employment contract types; an inflationary expectations data set and; a survey of students' evaluations of teaching. These three examples are chosen as they represent different values that the additional ancillary parameters are likely to take in practice.

Suggested Citation

  • Tim R.L. Fry & Mark N. Harris, 2002. "The DOGEV Model," Monash Econometrics and Business Statistics Working Papers 7/02, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2002-7
    as

    Download full text from publisher

    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2002/wp7-02.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kannan, P. K. & Yim, Chi Kin (Bennett), 2001. "An investigation of the impact of promotions on across-submarket competition," Journal of Business Research, Elsevier, vol. 53(3), pages 137-149, September.
    2. Tse, Y K, 1987. "A Diagnostic Test for the Multinomial Logit Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(2), pages 283-286, April.
    3. Marc J. I. Gaudry, 1980. "Dogit and Logit Models of Travel Mode Choice in Montreal," Canadian Journal of Economics, Canadian Economics Association, vol. 13(2), pages 268-279, May.
    4. 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.
    5. Swait, Joffre, 2001. "Choice set generation within the generalized extreme value family of discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 643-666, August.
    6. Fry, T R L, et al, 1993. "Economic Motivations for Limited Dependent and Qualitative Variable Models," The Economic Record, The Economic Society of Australia, vol. 69(205), pages 193-205, June.
    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. R. Aaberge & U. Colombino & T. Wennemo, 2009. "Evaluating Alternative Representations Of The Choice Sets In Models Of Labor Supply," Journal of Economic Surveys, Wiley Blackwell, vol. 23(3), pages 586-612, July.
    2. Gerunov, Anton, 2014. "Критичен Преглед На Основните Подходи За Моделиране На Икономическите Очаквания [A Critical Review of Major Approaches for Modeling Economic Expectations]," MPRA Paper 68797, University Library of Munich, Germany.
    3. Eyob Fissuh & Mark Harris, 2004. "Determinants of Poverty in Eritrea: A Household level Analysis," Econometric Society 2004 Australasian Meetings 364, Econometric Society.

    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. Fry, Tim R. L. & Harris, Mark N., 1996. "A Monte Carlo study of tests for the independence of irrelevant alternatives property," Transportation Research Part B: Methodological, Elsevier, vol. 30(1), pages 19-30, February.
    2. Nchare, Karim, 2021. "Dogit model and rational inattention," Economics Letters, Elsevier, vol. 205(C).
    3. Thomas J. Steenburgh, 2008. "The Invariant Proportion of Substitution Property (IPS) of Discrete-Choice Models," Marketing Science, INFORMS, vol. 27(2), pages 300-307, 03-04.
    4. Sasic, Ana & Habib, Khandker Nurul, 2013. "Modelling departure time choices by a Heteroskedastic Generalized Logit (Het-GenL) model: An investigation on home-based commuting trips in the Greater Toronto and Hamilton Area (GTHA)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 50(C), pages 15-32.
    5. Zhang, Junyi & Kuwano, Masashi & Lee, Backjin & Fujiwara, Akimasa, 2009. "Modeling household discrete choice behavior incorporating heterogeneous group decision-making mechanisms," Transportation Research Part B: Methodological, Elsevier, vol. 43(2), pages 230-250, February.
    6. Mai, Tien, 2016. "A method of integrating correlation structures for a generalized recursive route choice model," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 146-161.
    7. Bhat, Chandra R. & Guo, Jessica, 2004. "A mixed spatially correlated logit model: formulation and application to residential choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 38(2), pages 147-168, February.
    8. Daly, Andrew & Bierlaire, Michel, 2006. "A general and operational representation of Generalised Extreme Value models," Transportation Research Part B: Methodological, Elsevier, vol. 40(4), pages 285-305, May.
    9. Swait, Joffre & Brigden, Neil & Johnson, Richard D., 2014. "Categories shape preferences: A model of taste heterogeneity arising from categorization of alternatives," Journal of choice modelling, Elsevier, vol. 13(C), pages 3-23.
    10. T.R.L. Fry & R.D. Brooks & Br. Comley & J. Zhang, 1993. "Economic Motivations for Limited Dependent and Qualitative Variable Models," The Economic Record, The Economic Society of Australia, vol. 69(2), pages 193-205, June.
    11. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    12. Sarah Brown & Lisa Farrell & Mark N. Harris & John G. Sessions, 2006. "Risk preference and employment contract type," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 849-863, October.
    13. Shivaram Subramanian & Hanif Sherali, 2010. "A fractional programming approach for retail category price optimization," Journal of Global Optimization, Springer, vol. 48(2), pages 263-277, October.
    14. Allard, Ryan F. & Moura, Filipe, 2018. "Effect of transport transfer quality on intercity passenger mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 109(C), pages 89-107.
    15. Chorus, Caspar G., 2014. "Benefit of adding an alternative to one׳s choice set: A regret minimization perspective," Journal of choice modelling, Elsevier, vol. 13(C), pages 49-59.
    16. Dam, Tien Thanh & Ta, Thuy Anh & Mai, Tien, 2022. "Submodularity and local search approaches for maximum capture problems under generalized extreme value models," European Journal of Operational Research, Elsevier, vol. 300(3), pages 953-965.
    17. Florian Heiss & Stephan Hetzenecker & Maximilian Osterhaus, 2019. "Nonparametric Estimation of the Random Coefficients Model: An Elastic Net Approach," Papers 1909.08434, arXiv.org, revised Sep 2019.
    18. Coltman, Tim & Devinney, Timothy M. & Keating, Byron W., 2010. "Best-worst scaling approach to predict customer choice for 3PL services," MPRA Paper 40492, University Library of Munich, Germany.
    19. Marzano, Vittorio & Papola, Andrea, 2008. "On the covariance structure of the Cross-Nested Logit model," Transportation Research Part B: Methodological, Elsevier, vol. 42(2), pages 83-98, February.
    20. Kaplan, Sigal & Shiftan, Yoram & Bekhor, Shlomo, 2012. "Development and estimation of a semi-compensatory model with a flexible error structure," Transportation Research Part B: Methodological, Elsevier, vol. 46(2), pages 291-304.

    More about this item

    Keywords

    Generalized extreme value; Choice set generation; Ordinal data;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

    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:msh:ebswps:2002-7. 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: Professor Xibin Zhang (email available below). General contact details of provider: https://edirc.repec.org/data/dxmonau.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.