Kernel-based semiparametric multinomial logit modelling of political party affiliation

TL;DR

This paper introduces a semiparametric multinomial logit model with a smoothed likelihood estimator to analyze complex voter profiles, revealing nonlinear effects and interactions in political party affiliation data.

Contribution

It develops a novel smoothed likelihood estimator for semiparametric multinomial logit models, enhancing analysis of complex, nuanced voter behavior.

Findings

Strong interactions between age and income on voting likelihood

Highly nonlinear effects of covariates on party choice

Method applicable to other fields like marketing

Abstract

Conventional, parametric multinomial logit models are in general not sufficient for detecting the complex patterns voter profiles nowadays typically exhibit. In this manuscript, we use a semiparametric multinomial logit model to give a detailed analysis of the composition of a subsample of the German electorate in 2006. Germany is a particularly strong case for more flexible nonparametric approaches in this context, since due to the reunification and the preceding different political histories the composition of the electorate is very complex and nuanced. Our analysis reveals strong interactions of the covariates age and income, and highly nonlinear shapes of the factor impacts for each party's likelihood to be voted. Notably, we develop and provide a smoothed likelihood estimator for semiparametric multinomial logit models, which can be applied also in other application fields, such as, e.g., marketing.