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Identification and diagnosis of specification errors in the multinomial logit model

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  • Horowitz, Joel

Abstract

Multinomial logit models of travel demand are subject to a variety of specification errors that can severely affect the accuracy the models' forecasts. This paper describes some of the important sources of specification errors in logit models, presents numerical illustrations of the errors' effects on forecasts of travel behavior, and evaluates the ability of several informal and formal statistical procedures to identify and diagnose the errors. Among the tests considered, the most powerful are a test against a probit model and the McFadden, Tye and Train (1976) test based on the universal logit method. The least powerful tests are a test based on extrapolating the logit model and informal tests based on examination of the signs, t-statistics and ratios of the estimated parameters of the logit function.

Suggested Citation

  • Horowitz, Joel, 1981. "Identification and diagnosis of specification errors in the multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 15(5), pages 345-360, October.
    • Handle: RePEc:eee:transb:v:15:y:1981:i:5:p:345-360
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    Cited by:

    1. Arjun Seshadri & Johan Ugander, 2020. "Fundamental Limits of Testing the Independence of Irrelevant Alternatives in Discrete Choice," Papers 2001.07042, arXiv.org.
    2. Gensch, D. H. & Ghose, S., 1997. "Differences in independence of irrelevant alternatives at individual vs aggregate levels, and at single pair vs full choice set," Omega, Elsevier, vol. 25(2), pages 201-214, April.
    3. Tomás del Barrio Casto & William Nilsson & Andrés J. Picazo-Tadeo, 2013. "How wrong can you be, without noticing? Further evidence on speci?cation errors in the Conditional Logit," Working Papers 1318, Department of Applied Economics II, Universidad de Valencia.
    4. Hensher, David A., 2007. "Bus transport: Economics, policy and planning," Research in Transportation Economics, Elsevier, vol. 18(1), pages 1-507, January.
    5. Fischer, M.M. & Nijkamp, P., 1985. "Explanatory discrete spatial data and choice analysis : a state-of-the-art review," Serie Research Memoranda 0006, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    6. Mokhtarian, Patricia L., 2016. "Presenting the Independence of Irrelevant Alternatives property in a first course on logit modeling," Journal of choice modelling, Elsevier, vol. 21(C), pages 25-29.
    7. Tim R. L. Fry & Mark N. Harris, 1998. "Testing for Independence of Irrelevant Alternatives," Sociological Methods & Research, , vol. 26(3), pages 401-423, February.
    8. 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.
    9. Sebri, Maamar, 2015. "Use renewables to be cleaner: Meta-analysis of the renewable energy consumption–economic growth nexus," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 657-665.
    10. Bhat, Chandra R., 1995. "A heteroscedastic extreme value model of intercity travel mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 29(6), pages 471-483, December.
    11. W C Halperin & G D Richardson & N Gale & C M Costanzo, 1984. "A Generalized Procedure for Comparing Models of Spatial Choice," Environment and Planning A, , vol. 16(10), pages 1289-1301, October.
    12. Langche Zeng, 2000. "A Heteroscedastic Generalized Extreme Value Discrete Choice Model," Sociological Methods & Research, , vol. 29(1), pages 118-144, August.
    13. Sener, Ipek N. & Pendyala, Ram M. & Bhat, Chandra R., 2011. "Accommodating spatial correlation across choice alternatives in discrete choice models: an application to modeling residential location choice behavior," Journal of Transport Geography, Elsevier, vol. 19(2), pages 294-303.

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