Artikel
Inference on semiparametric multinomial response models
We explore inference on regression coefficients in semiparametric multinomial response models. We consider cross-sectional, and both static and dynamic panel settings where we focus throughout on inference under sufficient conditions for point identification. The approach to identification uses a matching insight throughout all three models coupled with variation in regressors: with cross-section data, we match across individuals while with panel data, we match within individuals over time. Across models, we relax the Indpendence of Irrelevant Alternatives (or IIA assumption, see McFadden (1974)) and allow for arbitrary correlation in the unobservables that determine utility of various alternatives. For the cross-sectional model, estimation is based on a localized rank objective function, analogous to that used in Abrevaya, Hausman, and Khan (2010), and presents a generalization of existing approaches. In panel data settings, rates of convergence are shown to exhibit a curse of dimensionality in the number of alternatives. The results for the dynamic panel data model generalize the work of Honoré and Kyriazidou (2000) to cover the semiparametric multinomial case. A simulation study establishes adequate finite sample properties of our new procedures. We apply our estimators to a scanner panel data set.
- Sprache
-
Englisch
- Erschienen in
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Journal: Quantitative Economics ; ISSN: 1759-7331 ; Volume: 12 ; Year: 2021 ; Issue: 3 ; Pages: 743-777 ; New Haven, CT: The Econometric Society
- Klassifikation
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Wirtschaft
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
Multiple or Simultaneous Equation Models: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
- Thema
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dynamic panel data
Multinomial response
rank estimation
- Ereignis
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Geistige Schöpfung
- (wer)
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Khan, Shakeeb
Ouyang, Fu
Tamer, Elie T.
- Ereignis
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Veröffentlichung
- (wer)
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The Econometric Society
- (wo)
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New Haven, CT
- (wann)
-
2021
- DOI
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doi:10.3982/QE1315
- Handle
- Letzte Aktualisierung
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10.03.0003, 11:19 MEZ
Datenpartner
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Objekttyp
- Artikel
Beteiligte
- Khan, Shakeeb
- Ouyang, Fu
- Tamer, Elie T.
- The Econometric Society
Entstanden
- 2021
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