Separating predicted randomness from residual behaviour
We propose a novel measure of goodness of fit for stochastic choice models, that is, the maximal fraction of data that can be reconciled with the model. The procedure is to separate the data into two parts: one generated by the best specification of the model and another representing residual behavi...
| Main Authors: | , |
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| Format: | Journal article |
| Language: | English |
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Oxford University Press
2020
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| _version_ | 1797064380872916992 |
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| author | Aspetugiua, J Ballester, M |
| author_facet | Aspetugiua, J Ballester, M |
| author_sort | Aspetugiua, J |
| collection | OXFORD |
| description | We propose a novel measure of goodness of fit for stochastic choice models, that is, the maximal fraction of data that can be reconciled with the model. The procedure is to separate the data into two parts: one generated by the best specification of the model and another representing residual behavior. We claim that the three elements involved in a separation are instrumental in understanding the data. We show how to apply our approach to any stochastic choice model and then study the case of four well-known models, each capturing a different notion of randomness. We illustrate our results with an experimental data set. |
| first_indexed | 2024-03-06T21:13:28Z |
| format | Journal article |
| id | oxford-uuid:3ef56b28-56a4-48e6-9f06-fcec74955e2c |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T21:13:28Z |
| publishDate | 2020 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | oxford-uuid:3ef56b28-56a4-48e6-9f06-fcec74955e2c2022-03-26T14:28:59ZSeparating predicted randomness from residual behaviourJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:3ef56b28-56a4-48e6-9f06-fcec74955e2cEnglishSymplectic ElementsOxford University Press2020Aspetugiua, JBallester, MWe propose a novel measure of goodness of fit for stochastic choice models, that is, the maximal fraction of data that can be reconciled with the model. The procedure is to separate the data into two parts: one generated by the best specification of the model and another representing residual behavior. We claim that the three elements involved in a separation are instrumental in understanding the data. We show how to apply our approach to any stochastic choice model and then study the case of four well-known models, each capturing a different notion of randomness. We illustrate our results with an experimental data set. |
| spellingShingle | Aspetugiua, J Ballester, M Separating predicted randomness from residual behaviour |
| title | Separating predicted randomness from residual behaviour |
| title_full | Separating predicted randomness from residual behaviour |
| title_fullStr | Separating predicted randomness from residual behaviour |
| title_full_unstemmed | Separating predicted randomness from residual behaviour |
| title_short | Separating predicted randomness from residual behaviour |
| title_sort | separating predicted randomness from residual behaviour |
| work_keys_str_mv | AT aspetugiuaj separatingpredictedrandomnessfromresidualbehaviour AT ballesterm separatingpredictedrandomnessfromresidualbehaviour |