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Parameter bias in misspecified Hybrid Choice Models: An empirical study.
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics.ORCID iD: 0000-0003-4512-9054
2018 (English)In: Transportation Research Procedia, Elsevier B.V. , 2018, p. 99-106Conference paper, Published paper (Refereed)
Abstract [en]

Model misspecification is likely to occur when working with real datasets. However, previous studies showing the advantages of hybrid choice models have mostly used models where structural and measurement equations match the functions employed in the data generating process, especially when parameter biases were discussed. The aim of this study is to investigate the extent of parameter bias in misspecified hybrid choice models, and assess if different modelling assumptions impact the parameter estimates of the choice model. For this task, a mode choice model is estimated on synthetic data with efforts focus on mimicking the conditions present in real datasets, where the postulated structural and measurement equations are less flexible than the functions used to generate the data. Results show that hybrid choice models, even if misspecified, manage to recover better parameter estimates than a multinomial logit. However, hybrid choice models are not unbeatable, as results also indicate that misspecified hybrid choice models might still yield biased parameter estimates. Moreover, results suggest that hybrid choice models successfully isolate the source of model bias, preventing its propagation to other parameter estimates. Results also show that parameter estimates from hybrid choice models are sensible to modelling assumptions, and that parameter estimates of the utility function are robust given that errors are modelled.

Place, publisher, year, edition, pages
Elsevier B.V. , 2018. p. 99-106
Keywords [en]
Hybrid Choice Models (HCM), Integrated Choice, Latent Variable models (ICLV), Latent variables, Mode choice, Model misspecification, Parameter bias, Synthetic dataset
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-247474DOI: 10.1016/j.trpro.2018.10.081Scopus ID: 2-s2.0-85057150519OAI: oai:DiVA.org:kth-247474DiVA, id: diva2:1302581
Conference
13th Conference on Transport Engineering, CIT 2018, 6 June 2018 through 8 June 2018
Note

QC20190405

Available from: 2019-04-05 Created: 2019-04-05 Last updated: 2019-04-05Bibliographically approved

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Lorenzo Varela, Juan Manuel

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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