IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v27y2008i2p300-307.html
   My bibliography  Save this article

The Invariant Proportion of Substitution Property (IPS) of Discrete-Choice Models

Author

Listed:
  • Thomas J. Steenburgh

    (Harvard Business School, Boston, Massachusetts 02163)

Abstract

This article introduces a newly discovered property of discrete-choice models, which I call the invariant proportion of substitution (IPS). Like the independence from irrelevant alternatives (IIA) property, IPS implies individual behavior that is counterintuitive in the context of choice among similar alternatives. But models that alleviate the concerns raised by IIA, such as generalized extreme value and covariance probit models, do not necessarily alleviate the concerns raised by IPS. I explore the implications of the IPS property on individual behavior in several choice contexts and discuss some models that alleviate the concerns raised by IPS.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ormksc:v:27:y:2008:i:2:p:300-307
    DOI: 10.1287/mksc.1070.0301
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.1070.0301
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.1070.0301?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. 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.
    2. Jeongwen Chiang, 1991. "A Simultaneous Approach to the Whether, What and How Much to Buy Questions," Marketing Science, INFORMS, vol. 10(4), pages 297-315.
    3. Wen, Chieh-Hua & Koppelman, Frank S., 2001. "The generalized nested logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 627-641, August.
    4. McFadden, Daniel L., 1984. "Econometric analysis of qualitative response models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 24, pages 1395-1457, Elsevier.
    5. 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.
    6. 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.
    7. Pradeep K. Chintagunta, 1993. "Investigating Purchase Incidence, Brand Choice and Purchase Quantity Decisions of Households," Marketing Science, INFORMS, vol. 12(2), pages 184-208.
    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. Qiang Liu & Thomas J. Steenburgh & Sachin Gupta, 2015. "The Cross Attributes Flexible Substitution Logit: Uncovering Category Expansion and Share Impacts of Marketing Instruments," Marketing Science, INFORMS, vol. 34(1), pages 144-159, January.
    2. Wiktor Adamowicz & David Bunch & Trudy Cameron & Benedict Dellaert & Michael Hanneman & Michael Keane & Jordan Louviere & Robert Meyer & Thomas Steenburgh & Joffre Swait, 2008. "Behavioral frontiers in choice modeling," Marketing Letters, Springer, vol. 19(3), pages 215-228, December.
    3. Peter Davis & Pasquale Schiraldi, 2014. "The flexible coefficient multinomial logit (FC-MNL) model of demand for differentiated products," RAND Journal of Economics, RAND Corporation, vol. 45(1), pages 32-63, March.
    4. Marije Schaafsma & Roy Brouwer, 2020. "Substitution Effects in Spatial Discrete Choice Experiments," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 75(2), pages 323-349, February.
    5. Xie, Yi, 2016. "The Impact of Nutrient Demand and Marketing Instruments on Intra-Category Substitution," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236051, Agricultural and Applied Economics Association.
    6. Thomas J. Steenburgh & Andrew Ainslie, 2008. "Taste Heterogeneity, IIA, and the Similarity Critique," Harvard Business School Working Papers 09-049, Harvard Business School.

    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. 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.
    2. 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.
    3. 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.
    4. Chikaraishi, Makoto & Nakayama, Shoichiro, 2016. "Discrete choice models with q-product random utilities," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 576-595.
    5. 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.
    6. Pinjari, Abdul Rawoof, 2011. "Generalized extreme value (GEV)-based error structures for multiple discrete-continuous choice models," Transportation Research Part B: Methodological, Elsevier, vol. 45(3), pages 474-489, March.
    7. 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.
    8. Bekhor, Shlomo & Prashker, Joseph N., 2008. "GEV-based destination choice models that account for unobserved similarities among alternatives," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 243-262, March.
    9. Tinessa, Fiore & Marzano, Vittorio & Papola, Andrea, 2020. "Mixing distributions of tastes with a Combination of Nested Logit (CoNL) kernel: Formulation and performance analysis," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 1-23.
    10. Meng, Qiang & Liu, Zhiyuan & Wang, Shuaian, 2012. "Optimal distance tolls under congestion pricing and continuously distributed value of time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(5), pages 937-957.
    11. 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.
    12. 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.
    13. Tien Mai & Patrick Jaillet, 2019. "Robust Product-line Pricing under Generalized Extreme Value Models," Papers 1912.09552, arXiv.org, revised Oct 2021.
    14. Tinessa, Fiore, 2021. "Closed-form random utility models with mixture distributions of random utilities: Exploring finite mixtures of qGEV models," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 262-288.
    15. Mogens Fosgerau & Julien Monardo & André de Palma, 2024. "The Inverse Product Differentiation Logit Model," American Economic Journal: Microeconomics, American Economic Association, vol. 16(4), pages 329-370, November.
    16. David R. Bell & Jeongwen Chiang & V. Padmanabhan, 1999. "The Decomposition of Promotional Response: An Empirical Generalization," Marketing Science, INFORMS, vol. 18(4), pages 504-526.
    17. Liang Guo, 2006. "—Removing the Boundary Between Structural and Reduced-Form Models," Marketing Science, INFORMS, vol. 25(6), pages 629-632, 11-12.
    18. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    19. Marshall Freimer & Dan Horsky, 2008. "Try It, You Will Like It—Does Consumer Learning Lead to Competitive Price Promotions?," Marketing Science, INFORMS, vol. 27(5), pages 796-810, 09-10.
    20. Nina Neubecker & Marcel Smolka & Anne Steinbacher, 2017. "Networks And Selection In International Migration To Spain," Economic Inquiry, Western Economic Association International, vol. 55(3), pages 1265-1286, July.

    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:inm:ormksc:v:27:y:2008:i:2:p:300-307. 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 Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.