Arbeitspapier
Nonseparable multinomial choice models in cross-section and panel data
Multinomial choice models are fundamental for empirical modeling of economic choices among discrete alternatives. We analyze identification of binary and multinomial choice models when the choice utilities are nonseparable in observed attributes and multidimensional unobserved heterogeneity with cross-section and panel data. We show that derivatives of choice probabilities with respect to continuous attributes are weighted averages of utility derivatives in cross-section models with exogenous heterogeneity. In the special case of random coefficient models with an independent additive effect, we further characterize that the probability derivative at zero is proportional to the population mean of the coefficients. We extend the identification results to models with endogenous heterogeneity using either a control function or panel data. In time stationary panel models with two periods, we find that differences over time of derivatives of choice probabilities identify utility derivatives "on the diagonal," i.e. when the observed attributes take the same values in the two periods. We also show that time stationarity does not identify structural derivatives "off the diagonal" both in continuous and multinomial choice panel models.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP33/17
- Klassifikation
-
Wirtschaft
- Thema
-
Multinomial choice
binary choice
nonseparable model
random coefficients
panel data
control function.
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Chernozhukov, Victor
Fernández-Val, Iván
Newey, Whitney K.
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre for Microdata Methods and Practice (cemmap)
- (wo)
-
London
- (wann)
-
2017
- DOI
-
doi:10.1920/wp.cem.2017.3317
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Chernozhukov, Victor
- Fernández-Val, Iván
- Newey, Whitney K.
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2017
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