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Identification and estimation of multinomial choice models with latent special covariates

Nail Kashaev

arXiv 13 Nov 2018 · Econometrics · publishedJournal of Business and Economic Statistics (2022) · 1 citations (OpenAlex)

arXiv:1811.05555 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Identification of multinomial choice models is often established by using special covariates that have full support. This paper shows how these identification results can be extended to a large class of multinomial choice models when all covariates are bounded. I also provide a new $\sqrt{n}$-consistent asymptotically normal estimator of the finite-dimensional parameters of the model.

Citation extraction

55
references
99
in-text mentions
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distinct cited
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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Fox, J. T., il Kim, K., Ryan, S. P., and Bajari, P (2012) The random coefficients logit model is identified1.00083100%
2Nevo, A (2000) A practitioner's guide to estimation of random-coefficients logit models of demand0.92844100%
3Lewbel, A (2000) Semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables0.84333100%
4Nevo, A (2001) Measuring market power in the ready-to-eat cereal industry0.84333100%
5Fox, J. T. and Gandhi, A (2016) Nonparametric identification and estimation of random coefficients in multinomial choice models0.81142100%
6Fox, J. T. and Lazzati, N (2017) A note on identification of discrete choice models for bundles and binary games0.73732100%
7Fox, J. T (2020) A note on nonparametric identification of distributions of random coefficients in multinomial choice models0.73732100%
8Lewbel, A., Yan, J., Zhou, Y., et al (2021) Semiparametric identification and estimation of multinomial discrete choice models using error symmetry0.73732100%
9Powell, J. L., Stock, J. H., and Stoker, T. M (1989) Semiparametric estimation of index coefficients0.73732100%
10Berry, S. T. and Haile, P. A (2009) Nonparametric identification of multinomial choice demand models with heterogeneous consumers0.64422100%

Showing the top 10 of 55 scored citations.