Richard T. Carson, Derrick H. Sun, Yixiao Sun
arXiv 13 May 2024 · Econometrics
arXiv:2405.08222 · PDF · DOI · OpenAlex · Extracted main text
At the core of most random utility models (RUMs) is an individual agent with a random utility component following a largest extreme value Type I (LEVI) distribution. What if, instead, the random component follows its mirror image -- the smallest extreme value Type I (SEVI) distribution? Differences between these specifications, closely tied to the random component's skewness, can be quite profound. For the same preference parameters, the two RUMs, equivalent with only two choice alternatives, diverge progressively as the number of alternatives increases, resulting in substantially different estimates and predictions for key measures, such as elasticities and market shares. The LEVI model imposes the well-known independence-of-irrelevant-alternatives property, while SEVI does not. Instead, the SEVI choice probability for a particular option involves enumerating all subsets that contain this option. The SEVI model, though more complex to estimate, is shown to have computationally tractable closed-form choice probabilities. Much of the paper delves into explicating the properties of the SEVI model and exploring implications of the random component's skewness. Conceptually, the difference between the LEVI and SEVI models centers on whether information, known only to the agent, is more likely to increase or decrease the systematic utility parameterized using observed attributes. LEVI does the former; SEVI the latter. An immediate implication is that if choice is characterized by SEVI random components, then the observed choice is more likely to correspond to the systematic-utility-maximizing choice than if characterized by LEVI. Examining standard empirical examples from different applied areas, we find that the SEVI model outperforms the LEVI model, suggesting the relevance of its inclusion in applied researchers' toolkits.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Train, K (2009) Discrete Choice Methods with Simulation | 0.928 | 4 | 3 | 100% |
| 2 | Fowlie, M (2010) Emissions trading, electricity restructuring, and investment in pollution abatement | 0.874 | 6 | 2 | 100% |
| 3 | Manski, C. F (1977) The structure of random utility models | 0.843 | 3 | 3 | 100% |
| 4 | Vuong, Q. H (1989) Likelihood ratio tests for model selection and non-nested hypotheses | 0.811 | 4 | 2 | 100% |
| 5 | Yellott, J. I (1977) The relationship between Luce's choice axiom, Thurstone's theory of comparative judgment, and the double exponential distribution | 0.737 | 4 | 2 | 75% |
| 6 | Block, H. and Marschak, J (1960) Random orderings and stochastic theories of responses | 0.737 | 3 | 2 | 100% |
| 7 | Sørensen, J. R.-V. and Fosgerau, M (2022) How McFadden met Rockafellar and learned to do more with less | 0.644 | 3 | 2 | 67% |
| 8 | Greene, W. H (2018) Econometric Analysis | 0.644 | 2 | 2 | 100% |
| 9 | Hausman, J. and McFadden, D (1984) Specification tests for the multinomial logit model | 0.644 | 2 | 2 | 100% |
| 10 | Hausman, J. and Wise, D (1977) Social experimentation, truncated distributions, and efficient estimation | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 51 scored citations.