IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v82y2015icp240-254.html
   My bibliography  Save this article

Critical assessment of five methods to correct for endogeneity in discrete-choice models

Author

Listed:
  • Guevara, C. Angelo

Abstract

Endogeneity often arises in discrete-choice models, precluding the consistent estimation of the model parameters, but it is habitually neglected in practical applications. The purpose of this article is to contribute in closing that gap by assessing five methods to address endogeneity in this context: the use of Proxys (PR); the two steps Control-Function (CF) method; the simultaneous estimation of the CF method via Maximum-Likelihood (ML); the Multiple Indicator Solution (MIS); and the integration of Latent-Variables (LV). The assessment is first made qualitatively, in terms of the formulation, normalization and data needs of each method. Then, the evaluation is made quantitatively, by means of a Monte Carlo experiment to study the finite sample properties under a unified data generation process, and to analyze the impact of common flaws. The methods studied differ notably in the range of problems that they can address; their underlying assumptions; the difficulty of gathering proper auxiliary variables needed to apply them; and their practicality, both in terms of the need for coding and their computational burden. The analysis developed in this article shows that PR is formally inappropriate for many cases, but it is easy to apply, and often corrects in the right direction. CF is also easy to apply with canned software, but requires instrumental variables which may be hard to collect in various contexts. Since CF is estimated in two stages, it may also compromise efficiency and difficult the estimation of standard errors. ML guarantees efficiency and direct estimation of the standard errors, but at the cost of larger computational burden required for the estimation of a multifold integral, with potential difficulties in identification, and retaining the difficulty of gathering proper instrumental variables. The MIS method appears relatively easy to apply and requiring indicators that may be easier to obtain in various cases. Finally, the LV approach appears as the more versatile method, but at a high cost in computational burden, problems of identification and limitations in the capability of writing proper structural equations for the latent variable.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:transa:v:82:y:2015:i:c:p:240-254
    DOI: 10.1016/j.tra.2015.10.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856415002554
    Download Restriction: Full text for ScienceDirect subscribers only

    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. Andrew Chesher, 2003. "Identification in Nonseparable Models," Econometrica, Econometric Society, vol. 71(5), pages 1405-1441, September.
    2. Guevara, Cristian Angelo & Thomas, Alan, 2007. "Multiple classification analysis in trip production models," Transport Policy, Elsevier, vol. 14(6), pages 514-522, November.
    3. Ferreira, Fernando, 2010. "You can take it with you: Proposition 13 tax benefits, residential mobility, and willingness to pay for housing amenities," Journal of Public Economics, Elsevier, vol. 94(9-10), pages 661-673, October.
    4. Walker, Joan & Ben-Akiva, Moshe, 2002. "Generalized random utility model," Mathematical Social Sciences, Elsevier, vol. 43(3), pages 303-343, July.
    5. repec:hrv:faseco:34728615 is not listed on IDEAS
    6. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    7. Rembert De Blander, 2008. "Which null hypothesis do overidentification restrictions actually test?," Economics Bulletin, AccessEcon, vol. 3(76), pages 1-9.
    8. Andrew Chesher, 2010. "Instrumental Variable Models for Discrete Outcomes," Econometrica, Econometric Society, vol. 78(2), pages 575-601, March.
    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. 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.
    11. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, January.
    12. repec:ebl:ecbull:v:3:y:2008:i:76:p:1-9 is not listed on IDEAS
    13. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    14. Matzkin, Rosa L., 2007. "Nonparametric identification," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 73 Elsevier.
    15. Joan L. Walker & Moshe Ben-Akiva & Denis Bolduc, 2007. "Identification of parameters in normal error component logit-mixture (NECLM) models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(6), pages 1095-1125.
    16. John M. Quigley, 1976. "Housing Demand in the Short Run: An Analysis of Polytomous Choice," NBER Chapters,in: Explorations in Economic Research, Volume 3, number 1, pages 76-102 National Bureau of Economic Research, Inc.
    17. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    18. Abay, Kibrom A. & Paleti, Rajesh & Bhat, Chandra R., 2013. "The joint analysis of injury severity of drivers in two-vehicle crashes accommodating seat belt use endogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 50(C), pages 74-89.
    19. 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.
    20. Cherchi, Elisabetta & Guevara, Cristian Angelo, 2012. "A Monte Carlo experiment to analyze the curse of dimensionality in estimating random coefficients models with a full variance–covariance matrix," Transportation Research Part B: Methodological, Elsevier, vol. 46(2), pages 321-332.
    21. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    22. K. Newey, Whitney, 1985. "Generalized method of moments specification testing," Journal of Econometrics, Elsevier, vol. 29(3), pages 229-256, September.
    23. Andrew Chesher, 2005. "Nonparametric Identification under Discrete Variation," Econometrica, Econometric Society, vol. 73(5), pages 1525-1550, September.
    24. Andrew Chesher & Adam M. Rosen & Konrad Smolinski, 2013. "An instrumental variable model of multiple discrete choice," Quantitative Economics, Econometric Society, vol. 4(2), pages 157-196, July.
    25. Glerum, Aurélie & Atasoy, Bilge & Bierlaire, Michel, 2014. "Using semi-open questions to integrate perceptions in choice models," Journal of choice modelling, Elsevier, vol. 10(C), pages 11-33.
    26. Elie Tamer, 2010. "Partial Identification in Econometrics," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 167-195, September.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    32. 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. Vij, Akshay & Walker, Joan L., 2016. "How, when and why integrated choice and latent variable models are latently useful," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 192-217.
    2. repec:eee:transb:v:109:y:2018:i:c:p:70-89 is not listed on IDEAS
    3. repec:eee:transa:v:100:y:2017:i:c:p:228-246 is not listed on IDEAS
    4. repec:eee:eejocm:v:26:y:2018:i:c:p:1-18 is not listed on IDEAS
    5. Palma, David & Ortúzar, Juan de Dios & Rizzi, Luis Ignacio & Guevara, Cristian Angelo & Casaubon, Gerard & Ma, Huiqin, 2016. "Modelling choice when price is a cue for quality: a case study with Chinese consumers," Journal of choice modelling, Elsevier, vol. 19(C), pages 24-39.
    6. Virginie Lurkin & Laurie A. Garrow & Matthew J. Higgins & Jeffrey P. Newman & Michael Schyns, 2016. "Accounting for Price Endogeneity in Airline Itinerary Choice Models: An Application to Continental U.S. Markets," NBER Working Papers 22730, National Bureau of Economic Research, Inc.
    7. repec:eee:jaitra:v:64:y:2017:i:pa:p:91-99 is not listed on IDEAS
    8. Johannes Dahlin & Verena Halbherr & Peter Kurz & Michael Nelles & Carsten Herbes, 2016. "Marketing Green Fertilizers: Insights into Consumer Preferences," Sustainability, MDPI, Open Access Journal, vol. 8(11), pages 1-15, November.
    9. repec:kap:transp:v:45:y:2018:i:2:d:10.1007_s11116-017-9851-6 is not listed on IDEAS
    10. 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.
    11. repec:kap:transp:v:44:y:2017:i:5:d:10.1007_s11116-016-9689-3 is not listed on IDEAS

    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:transa:v:82:y:2015:i:c:p:240-254. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.