IDEAS home Printed from https://ideas.repec.org/a/eee/transb/v123y2019icp224-239.html
   My bibliography  Save this article

A control-function approach to correct for endogeneity in discrete choice models estimated on SP-off-RP data and contrasts with an earlier FIML approach by Train & Wilson

Author

Listed:
  • Guevara, C. Angelo
  • Hess, Stephane

Abstract

It is common practice to build Stated Preference (SP) attributes and alternatives from observed Revealed Preference (RP) choices with a view to increasing realism. While many surveys pivot all alternatives around an observed choice, others use more adaptive approaches in which changes are made depending on what alternative was chosen in the RP setting. For example, in SP-off-RP data, the alternative chosen in the RP setting is worsened in the SP setting and other alternatives are improved to induce a change in behaviour. This facilitates the creation of meaningful trade-offs or tipping points but introduces endogeneity. This source of endogeneity was largely ignored until Train and Wilson (T&W) proposed a full information maximum likelihood (FIML) solution that can be implemented with simulation. In this article, we propose a limited information maximum likelihood (LIML) approach to address the SP-off-RP problem using a method which does not need simulation, can be applied with standard software and uses data that is already available for the stated problem. The proposed method is an application of the control-function (CF) method to correct for endogeneity in discrete choice models, using the RP attributes as instrumental variables. We discuss the theoretical and practical advantages and disadvantages of the CF and T&W methods and illustrate them using Monte Carlo and real data. Results show that, while the T&W method may be more efficient in theory, it may however fail to retrieve consistent estimators when it does not account properly for the data generation process if, e.g., an exogenous source of correlation among the SP choice tasks exists. On the other hand, the CF is more robust, i.e. less sensitive, to the data generation process assumptions, and is considerably easier to apply with standard software and does not require simulation, facilitating its adoption and the more extensive use of SP-off-RP data.

Suggested Citation

  • Guevara, C. Angelo & Hess, Stephane, 2019. "A control-function approach to correct for endogeneity in discrete choice models estimated on SP-off-RP data and contrasts with an earlier FIML approach by Train & Wilson," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 224-239.
    • Handle: RePEc:eee:transb:v:123:y:2019:i:c:p:224-239
    DOI: 10.1016/j.trb.2019.03.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0191261518309226
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.trb.2019.03.022?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. Hensher, David A., 2010. "Hypothetical bias, choice experiments and willingness to pay," Transportation Research Part B: Methodological, Elsevier, vol. 44(6), pages 735-752, July.
    2. John M. Rose & Michiel C.J. Bliemer, 2014. "Stated choice experimental design theory: the who, the what and the why," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 7, pages 152-177, Edward Elgar Publishing.
    3. 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.
    4. 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.
    5. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    6. Terza, Joseph V. & Basu, Anirban & Rathouz, Paul J., 2008. "Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling," Journal of Health Economics, Elsevier, vol. 27(3), pages 531-543, May.
    7. Cristian Angelo Guevara & Moshe E. Ben-Akiva, 2012. "Change of Scale and Forecasting with the Control-Function Method in Logit Models," Transportation Science, INFORMS, vol. 46(3), pages 425-437, August.
    8. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, November.
    9. Rivers, Douglas & Vuong, Quang H., 1988. "Limited information estimators and exogeneity tests for simultaneous probit models," Journal of Econometrics, Elsevier, vol. 39(3), pages 347-366, November.
    10. Guevara, C. Angelo, 2018. "Overidentification tests for the exogeneity of instruments in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 241-253.
    11. Pinar Karaca-Mandic & Kenneth Train, 2003. "Standard error correction in two-stage estimation with nested samples," Econometrics Journal, Royal Economic Society, vol. 6(2), pages 401-407, December.
    12. van Cranenburgh, S. & Chorus, C.G. & van Wee, B., 2014. "Vacation behaviour under high travel cost conditions – A stated preference of revealed preference approach," Tourism Management, Elsevier, vol. 43(C), pages 105-118.
    13. Amil Petrin & Kenneth Train, 2003. "Omitted Product Attributes in Discrete Choice Models," NBER Working Papers 9452, National Bureau of Economic Research, Inc.
    14. Lee, Lung-Fei, 1982. "Specification error in multinomial logit models : Analysis of the omitted variable bias," Journal of Econometrics, Elsevier, vol. 20(2), pages 197-209, November.
    15. Shinghal, Nalin & Fowkes, Tony, 2002. "Freight mode choice and adaptive stated preferences," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 38(5), pages 367-378, September.
    16. Train, Kenneth & Wilson, Wesley W., 2008. "Estimation on stated-preference experiments constructed from revealed-preference choices," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 191-203, March.
    17. Ruud, Paul A, 1983. "Sufficient Conditions for the Consistency of Maximum Likelihood Estimation Despite Misspecifications of Distribution in Multinomial Discrete Choice Models," Econometrica, Econometric Society, vol. 51(1), pages 225-228, January.
    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. Danaf, Mazen & Guevara, C. Angelo & Ben-Akiva, Moshe, 2023. "A control-function correction for endogeneity in random coefficients models: The case of choice-based recommender systems," Journal of choice modelling, Elsevier, vol. 46(C).
    2. David A. Hensher & Camila Balbontin & William H. Greene & Joffre Swait, 2021. "Experience as a conditioning effect on choice: Does it matter whether it is exogenous or endogenous?," Transportation, Springer, vol. 48(5), pages 2825-2855, October.
    3. de Grange, Louis & González, Felipe & Marechal, Matthieu & Troncoso, Rodrigo, 2024. "Estimating multinomial logit models with endogenous variables: Control function versus two adapted approaches," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    4. Haghani, Milad & Bliemer, Michiel C.J. & Rose, John M. & Oppewal, Harmen & Lancsar, Emily, 2021. "Hypothetical bias in stated choice experiments: Part II. Conceptualisation of external validity, sources and explanations of bias and effectiveness of mitigation methods," Journal of choice modelling, Elsevier, vol. 41(C).
    5. Fukushi, Mitsuyoshi & Delgado, Felipe & Raveau, Sebastián, 2024. "Impact of omitted variable and simultaneous estimation endogeneity in choice-based revenue management systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    6. Biswas, Mehek & Bhat, Chandra R. & Pinjari, Abdul Rawoof, 2024. "The use of pooled RP-SP choice data to simultaneously identify alternative attributes and random coefficients on those attributes," Transportation Research Part B: Methodological, Elsevier, vol. 186(C).
    7. Watanabe, Hajime & Maruyama, Takuya, 2023. "A Bayesian instrumental variable model for multinomial choice with correlated alternatives," Journal of choice modelling, Elsevier, vol. 46(C).
    8. Danaf, Mazen & Guevara, Angelo & Atasoy, Bilge & Ben-Akiva, Moshe, 2020. "Endogeneity in adaptive choice contexts: Choice-based recommender systems and adaptive stated preferences surveys," Journal of choice modelling, Elsevier, vol. 34(C).
    9. Balbontin, Camila & Hensher, David A. & Beck, Matthew J., 2024. "The influence of working from home and underlying attitudes on the number of commuting and non-commuting trips by workers during 2020 and 2021 pre- and post-lockdown in Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    10. Fowri, Hamid R. & Seyedabrishami, Seyedehsan, 2020. "Assessment of urban transportation pricing policies with incorporation of unobserved heterogeneity," Transport Policy, Elsevier, vol. 99(C), pages 12-19.
    11. Sarrias, Mauricio, 2021. "A two recursive equation model to correct for endogeneity in latent class binary probit models," Journal of choice modelling, Elsevier, vol. 40(C).
    12. Afghari, Amir Pooyan & Faghih Imani, Ahmadreza & Papadimitriou, Eleonora & van Gelder, Pieter & Hezaveh, Amin Mohamadi, 2021. "Disentangling the effects of unobserved factors on seatbelt use choices in multi-occupant vehicles," Journal of choice modelling, Elsevier, vol. 41(C).
    13. Milad Haghani & Michiel C. J. Bliemer & John M. Rose & Harmen Oppewal & Emily Lancsar, 2021. "Hypothetical bias in stated choice experiments: Part II. Macro-scale analysis of literature and effectiveness of bias mitigation methods," Papers 2102.02945, arXiv.org.
    14. Sander van Cranenburgh & Francisco Garrido-Valenzuela, 2023. "Computer vision-enriched discrete choice models, with an application to residential location choice," Papers 2308.08276, arXiv.org.

    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. Guevara, C. Angelo, 2018. "Overidentification tests for the exogeneity of instruments in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 241-253.
    2. 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.
    3. Gopalakrishnan, Raja & Guevara, C. Angelo & Ben-Akiva, Moshe, 2020. "Combining multiple imputation and control function methods to deal with missing data and endogeneity in discrete-choice models," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 45-57.
    4. Thomas E. Guerrero & C. Angelo Guevara & Elisabetta Cherchi & Juan de Dios Ortúzar, 2021. "Addressing endogeneity in strategic urban mode choice models," Transportation, Springer, vol. 48(4), pages 2081-2102, August.
    5. Guevara, C. Angelo & Tirachini, Alejandro & Hurtubia, Ricardo & Dekker, Thijs, 2020. "Correcting for endogeneity due to omitted crowding in public transport choice using the Multiple Indicator Solution (MIS) method," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 472-484.
    6. Fernández-Antolín, Anna & Guevara, C. Angelo & de Lapparent, Matthieu & Bierlaire, Michel, 2016. "Correcting for endogeneity due to omitted attitudes: Empirical assessment of a modified MIS method using RP mode choice data," Journal of choice modelling, Elsevier, vol. 20(C), pages 1-15.
    7. Cristian Angelo Guevara & Moshe E. Ben-Akiva, 2012. "Change of Scale and Forecasting with the Control-Function Method in Logit Models," Transportation Science, INFORMS, vol. 46(3), pages 425-437, August.
    8. Danaf, Mazen & Guevara, C. Angelo & Ben-Akiva, Moshe, 2023. "A control-function correction for endogeneity in random coefficients models: The case of choice-based recommender systems," Journal of choice modelling, Elsevier, vol. 46(C).
    9. Sarrias, Mauricio, 2021. "A two recursive equation model to correct for endogeneity in latent class binary probit models," Journal of choice modelling, Elsevier, vol. 40(C).
    10. de Grange, Louis & González, Felipe & Marechal, Matthieu & Troncoso, Rodrigo, 2024. "Estimating multinomial logit models with endogenous variables: Control function versus two adapted approaches," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    11. Guerrero, Thomas E. & Guevara, C. Angelo & Cherchi, Elisabetta & Ortúzar, Juan de Dios, 2022. "Characterizing the impact of discrete indicators to correct for endogeneity in discrete choice models," Journal of choice modelling, Elsevier, vol. 42(C).
    12. Fukushi, Mitsuyoshi & Delgado, Felipe & Raveau, Sebastián, 2024. "Impact of omitted variable and simultaneous estimation endogeneity in choice-based revenue management systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    13. Danaf, Mazen & Guevara, Angelo & Atasoy, Bilge & Ben-Akiva, Moshe, 2020. "Endogeneity in adaptive choice contexts: Choice-based recommender systems and adaptive stated preferences surveys," Journal of choice modelling, Elsevier, vol. 34(C).
    14. Lurkin, Virginie & Garrow, Laurie A. & Higgins, Matthew J. & Newman, Jeffrey P. & Schyns, Michael, 2017. "Accounting for price endogeneity in airline itinerary choice models: An application to Continental U.S. markets," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 228-246.
    15. Manuel Denzer, 2019. "Estimating Causal Effects in Binary Response Models with Binary Endogenous Explanatory Variables - A Comparison of Possible Estimators," Working Papers 1916, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    16. Guevara, C. Angelo & Tang, Yue & Gao, Song, 2018. "The initial condition problem with complete history dependency in learning models for travel choices," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 850-861.
    17. Helveston, John Paul & Feit, Elea McDonnell & Michalek, Jeremy J., 2018. "Pooling stated and revealed preference data in the presence of RP endogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 109(C), pages 70-89.
    18. Pike, Susan & Lubell, Mark, 2018. "The conditional effects of social influence in transportation mode choice," Research in Transportation Economics, Elsevier, vol. 68(C), pages 2-10.
    19. Maness, Michael & Cirillo, Cinzia, 2016. "An indirect latent informational conformity social influence choice model: Formulation and case study," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 75-101.
    20. Giampiero Marra & Matteo Fasiolo & Rosalba Radice & Rainer Winkelmann, 2023. "A flexible copula regression model with Bernoulli and Tweedie margins for estimating the effect of spending on mental health," Health Economics, John Wiley & Sons, Ltd., vol. 32(6), pages 1305-1322, June.

    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:transb:v:123:y:2019:i:c:p:224-239. 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.elsevier.com/wps/find/journaldescription.cws_home/548/description#description .

    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.