Purpose

Public management researchers commonly model dichotomous dependent variables with parametric methods despite their relatively strong assumptions about the data generating process. Without testing for those assumptions and consideration of semiparametric alternatives, such as maximum score, estimates might be biased, or predictions might not be as accurate as possible.

Design/methodology/approach

To guide researchers, this paper provides an evaluative framework for comparing parametric estimators with semiparametric and nonparametric estimators for dichotomous dependent variables. To illustrate the framework, the article estimates the factors associated with the passage of school district bond referenda in all Texas school districts from 1998 to 2015.

Findings

Estimates show that the correct prediction of a bond passing increases from 77.2 to 78%, with maximum score estimation relative to a commonly used parametric alternative. While this is a small increase, it is meaningful in comparison to the random prediction base model.

Originality/value

Future research modeling any dichotomous dependent variable can use the framework to identify the most appropriate estimator and relevant statistical programs.

中文翻译：

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在公共管理研究中超越参数回归

目的

尽管公共管理研究人员对数据生成过程有相对强烈的假设，但他们通常使用参数方法对二分因变量进行建模。如果不测试这些假设并考虑半参数替代方案（例如最大分数），估计可能会有偏差，或者预测可能不会尽可能准确。

设计/方法/方法

为了指导研究人员，本文提供了一个评估框架，用于比较二分因变量的参数估计量与半参数和非参数估计量。为了说明该框架，本文估计了与 1998 年至 2015 年德克萨斯州所有学区通过学区债券公投相关的因素。

发现

估计表明，债券通过的正确预测从 77.2% 增加到 78%，相对于常用参数替代方案的最大得分估计。虽然这是一个很小的增加，但与随机预测基础模型相比，它是有意义的。

原创性/价值

未来对任何二分因变量建模的研究可以使用该框架来确定最合适的估计量和相关的统计程序。