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Modeling Discrete Choice with Uncertain Data: An Augmented MNL Estimator
Author(s) -
Hellerstein Daniel
Publication year - 2005
Publication title -
american journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.949
H-Index - 111
eISSN - 1467-8276
pISSN - 0002-9092
DOI - 10.1111/j.0002-9092.2005.00703.x
Subject(s) - estimator , multinomial logistic regression , respondent , discrete choice , econometrics , choice set , set (abstract data type) , multinomial distribution , mixed logit , computer science , data set , logit , control (management) , statistics , mathematical optimization , logistic regression , mathematics , artificial intelligence , political science , law , programming language
This article introduces a multinomial logit model that uses ancillary information to control for uncertainty in both the observed choices made by respondents, and in the attributes of a respondent's choice set. Simulated data are used to compare the performance of this estimator versus simpler models, under several different kinds of uncertainty.