Iterative Distributed Multinomial Regression

Yanqin Fan, Yigit Okar and Xuetao Shi

Papers from arXiv.org

Abstract: This article introduces an iterative distributed computing estimator for the multinomial logistic regression model with large choice sets. Compared to the maximum likelihood estimator, the proposed iterative distributed estimator achieves significantly faster computation and, when initialized with a consistent estimator, attains asymptotic efficiency under a weak dominance condition. Additionally, we propose a parametric bootstrap inference procedure based on the iterative distributed estimator and establish its consistency. Extensive simulation studies validate the effectiveness of the proposed methods and highlight the computational efficiency of the iterative distributed estimator.

Date: 2024-12
New Economics Papers: this item is included in nep-dcm and nep-ecm
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