Working Paper: NBER ID: w14934
Authors: Patrick Bajari; Jeremy Fox; Kyoo Il Kim; Stephen P. Ryan
Abstract: The random coefficients, multinomial choice logit model has been widely used in empirical choice analysis for the last 30 years. We are the first to prove that the distribution of random coefficients in this model is nonparametrically identified. Our approach exploits the structure of the logit model, and so requires no monotonicity assumptions and requires variation in product characteristics within only an infinitesimally small open set. Our identification argument is constructive and may be applied to other choice models with random coefficients.
Keywords: Random Coefficients; Logit Model; Identification; Nonparametric Estimation
JEL Codes: C14; C25; L00
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| variation in product characteristics (L15) | identification of the density function (C46) |
| choice probability operator is one-to-one (C25) | identification of the density function (C46) |
| identification of the density function (C46) | consistency of nonparametric estimators (C51) |