Abstract

In this article, we propose generalized two-parameter (GTP) estimators and an algorithm for the estimation of shrinkage parameters to combat multicollinearity in the multinomial logit regression model. In addition, the mean squared error properties of the estimators are derived. A simulation study is conducted to investigate the performance of proposed estimators for different sample sizes, degrees of multicollinearity, and the number of explanatory variables. Swedish football league dataset is analyzed to show the benefits of the GTP estimators over the traditional maximum likelihood estimator (MLE). The empirical results of this article revealed that GTP estimators have a smaller mean squared error than the MLE and can be recommended for practitioners.

摘要

在本文中，我们提出了广义二参数 (GTP) 估计器和收缩参数估计算法，以对抗多项 Logit 回归模型中的多重共线性。此外，还导出了估计量的均方误差特性。进行模拟研究是为了研究所提出的估计量在不同样本量、多重共线性程度和解释变量数量下的性能。对瑞典足球联赛数据集进行分析，以显示 GTP 估计器相对于传统最大似然估计器 (MLE) 的优势。本文的实证结果表明，GTP 估计量的均方误差比 MLE 更小，可以推荐给实践者。