Estimating Semi-parametric Panel Multinomial Choice Models using Cyclic Monotonicity

TL;DR

This paper introduces a novel semi-parametric method for estimating panel multinomial choice models using cyclic monotonicity, enabling identification without distributional assumptions on utility shocks.

Contribution

It develops a new identification and estimation approach leveraging cyclic monotonicity, avoiding shape restrictions on the error distribution.

Findings

Provides a consistent estimator based on cyclic monotonicity inequalities.

Achieves point identification of model parameters under simple covariate assumptions.

Extends multinomial choice modeling to panel data with fixed effects.

Abstract

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.