  This paper proposes a new semi-parametric identification and estimation
approach to multinomial choice models in a panel data setting with individual
fixed effects. Our approach is based on cyclic monotonicity, which is a
defining feature of the random utility framework underlying multinomial choice
models. From the cyclic monotonicity property, we derive identifying
inequalities without requiring any shape restrictions for the distribution of
the random utility shocks. These inequalities point identify model parameters
under straightforward assumptions on the covariates. We propose a consistent
estimator based on these inequalities.
