IDEAS home Printed from https://ideas.repec.org/a/kap/transp/v48y2021i4d10.1007_s11116-020-10122-y.html
   My bibliography  Save this article

Addressing endogeneity in strategic urban mode choice models

Author

Listed:
  • Thomas E. Guerrero

    (Universidad Francisco de Paula Santander Ocaña
    Pontificia Universidad Católica de Chile
    Newcastle University)

  • C. Angelo Guevara

    (Universidad de Chile)

  • Elisabetta Cherchi

    (Newcastle University)

  • Juan de Dios Ortúzar

    (Pontificia Universidad Católica de Chile)

Abstract

Endogeneity is a potential anomaly in econometric models, which may cause inconsistent parameter estimates. Transport models are prone to this problem and applications that properly correct for it are scarce. This paper focuses on how to address this issue in the case of strategic urban mode choice models (i.e., the third stage of classic strategic transport models), possibly the main tool for the assessment of costly transport projects. To address this problem, we propose and validate, for the first time, adequate instruments that may be obtained from data that is already available in this context. The proposed method is implemented using the Control Function approach, which we use to detect and correct for endogeneity in a case study in Valparaiso, Chile. The effects arising from the neglected endogeneity in this case study reflect on an overestimation between 26–49% of the subjective value of time and an underestimation of 33–75% of modal elasticities.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:transp:v:48:y:2021:i:4:d:10.1007_s11116-020-10122-y
    DOI: 10.1007/s11116-020-10122-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11116-020-10122-y
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11116-020-10122-y?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. Gaudry, Marc J. I. & Jara-Diaz, Sergio R. & Ortuzar, Juan de Dios, 1989. "Value of time sensitivity to model specification," Transportation Research Part B: Methodological, Elsevier, vol. 23(2), pages 151-158, April.
    2. Stephane Hess & Nesha Beharry-Borg, 2012. "Accounting for Latent Attitudes in Willingness-to-Pay Studies: The Case of Coastal Water Quality Improvements in Tobago," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 52(1), pages 109-131, May.
    3. Jerry Hausman & Gregory Leonard & J. Douglas Zona, 1994. "Competitive Analysis with Differentiated Products," Annals of Economics and Statistics, GENES, issue 34, pages 143-157.
    4. repec:adr:anecst:y:1994:i:34:p:06 is not listed on IDEAS
    5. Luis I. Rizzi & Juan de Dios Ortúzar, 2006. "Road Safety Valuation under a Stated Choice Framework," Journal of Transport Economics and Policy, University of Bath, vol. 40(1), pages 69-94, January.
    6. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    7. 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.
    8. Varela, Juan Manuel Lorenzo & Börjesson, Maria & Daly, Andrew, 2018. "Quantifying errors in travel time and cost by latent variables," Working papers in Transport Economics 2018:3, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    9. Nevo, Aviv, 2001. "Measuring Market Power in the Ready-to-Eat Cereal Industry," Econometrica, Econometric Society, vol. 69(2), pages 307-342, March.
    10. Newey, Whitney K., 1987. "Efficient estimation of limited dependent variable models with endogenous explanatory variables," Journal of Econometrics, Elsevier, vol. 36(3), pages 231-250, November.
    11. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    12. Andrews,Donald W. K. & Stock,James H. (ed.), 2005. "Identification and Inference for Econometric Models," Cambridge Books, Cambridge University Press, number 9780521844413, January.
    13. Ricardo Hurtubia & Michel Bierlaire, 2014. "Estimation of Bid Functions for Location Choice and Price Modeling with a Latent Variable Approach," Networks and Spatial Economics, Springer, vol. 14(1), pages 47-65, March.
    14. Jerry A. Hausman, 1996. "Valuation of New Goods under Perfect and Imperfect Competition," NBER Chapters, in: The Economics of New Goods, pages 207-248, National Bureau of Economic Research, Inc.
    15. 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.
    16. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    17. Timothy F. Bresnahan & Robert J. Gordon, 1996. "The Economics of New Goods," NBER Books, National Bureau of Economic Research, Inc, number bres96-1.
    18. Amemiya, Takeshi, 1978. "The Estimation of a Simultaneous Equation Generalized Probit Model," Econometrica, Econometric Society, vol. 46(5), pages 1193-1205, September.
    19. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    20. Varela, Juan Manuel Lorenzo & Börjesson, Maria & Daly, Andrew, 2018. "Quantifying errors in travel time and cost by latent variables," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 520-541.
    21. 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.
    22. Varotto, Silvia F. & Glerum, Aurélie & Stathopoulos, Amanda & Bierlaire, Michel & Longo, Giovanni, 2017. "Mitigating the impact of errors in travel time reporting on mode choice modelling," Journal of Transport Geography, Elsevier, vol. 62(C), pages 236-246.
    23. Jeffrey M. Wooldridge, 2015. "Control Function Methods in Applied Econometrics," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 420-445.
    24. 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.
    25. Bass, Pablo & Donoso, Pedro & Munizaga, Marcela, 2011. "A model to assess public transport demand stability," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(8), pages 755-764, October.
    26. Yáñez, M.F. & Raveau, S. & Ortúzar, J. de D., 2010. "Inclusion of latent variables in Mixed Logit models: Modelling and forecasting," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(9), pages 744-753, November.
    27. 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.
    28. 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.
    29. Nelson Granados & Alok Gupta & Robert J. Kauffman, 2012. "Online and Offline Demand and Price Elasticities: Evidence from the Air Travel Industry," Information Systems Research, INFORMS, vol. 23(1), pages 164-181, March.
    30. J. Miguel Villas-Boas & Russell S. Winer, 1999. "Endogeneity in Brand Choice Models," Management Science, INFORMS, vol. 45(10), pages 1324-1338, October.
    31. 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.
    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. 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).
    2. 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).
    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. Perez-Lopez, Jose-Benito & Novales, Margarita & Orro, Alfonso, 2022. "Spatially correlated nested logit model for spatial location choice," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 1-12.

    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. 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.
    2. 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.
    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. 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.
    5. 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).
    6. 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.
    7. Hotle, Susan L. & Castillo, Marco & Garrow, Laurie A. & Higgins, Matthew J., 2015. "The impact of advance purchase deadlines on airline consumers’ search and purchase behaviors," Transportation Research Part A: Policy and Practice, Elsevier, vol. 82(C), pages 1-16.
    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. 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.
    10. Amil Petrin & Kenneth Train, 2003. "Omitted Product Attributes in Discrete Choice Models," NBER Working Papers 9452, National Bureau of Economic Research, Inc.
    11. 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.
    12. Steven T. Berry & Philip A. Haile, 2021. "Foundations of Demand Estimation," Cowles Foundation Discussion Papers 2301, Cowles Foundation for Research in Economics, Yale University.
    13. Carlos Pérez Montes, 2013. "The impact of interbank and public debt markets on the competition for bank deposits," Working Papers 1319, Banco de España.
    14. 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.
    15. Lurkin, Virginie & Garrow, Laurie A. & Higgins, Matthew J. & Newman, Jeffrey P. & Schyns, Michael, 2018. "Modeling competition among airline itineraries," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 157-172.
    16. Yonezawa, Koichi & Richards, Timothy J., 2016. "Competitive Package Size Decisions," Journal of Retailing, Elsevier, vol. 92(4), pages 445-469.
    17. Joanna Mazur & Katarzyna Śledziewska & Damian Zieba, 2018. "Regulation of Geo-blocking: does it address the problem of low intraEU iTrade?," Working Papers 2018-20, Faculty of Economic Sciences, University of Warsaw.
    18. Yang Li & Asim Ansari, 2014. "A Bayesian Semiparametric Approach for Endogeneity and Heterogeneity in Choice Models," Management Science, INFORMS, vol. 60(5), pages 1161-1179, May.
    19. Marshall Fisher & Santiago Gallino & Jun Li, 2018. "Competition-Based Dynamic Pricing in Online Retailing: A Methodology Validated with Field Experiments," Management Science, INFORMS, vol. 64(6), pages 2496-2514, June.
    20. 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).

    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:kap:transp:v:48:y:2021:i:4:d:10.1007_s11116-020-10122-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.