A causal approach to test empirical capital structure regularities

Q1 Mathematics
Simone Cenci , Stephen Kealhofer
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引用次数: 0

Abstract

Capital structure theories are often formulated as causal narratives to explain which factors drive financing choices. These narratives are usually examined by estimating cross–sectional relations between leverage and its determinants. However, the limitations of causal inference from observational data are often overlooked. To address this issue, we use structural causal modeling to identify how classic determinants of leverage are causally linked to capital structure and how this causal structure influences the effect-estimation process. The results provide support for the causal role of variables that measure the potential for information asymmetry concerning firms’ market values. Overall, our work provide a crucial step to connect capital structure theories with their empirical tests beyond simple correlations.

实证资本结构规律检验的因果方法
资本结构理论通常被表述为因果叙事,以解释哪些因素驱动融资选择。这些叙述通常通过估算杠杆及其决定因素之间的横截面关系来检验。然而,从观测数据进行因果推断的局限性常常被忽视。为了解决这个问题,我们使用结构因果模型来确定杠杆的经典决定因素如何与资本结构产生因果关系,以及这种因果结构如何影响效果估计过程。研究结果为衡量企业市场价值信息不对称可能性的变量的因果作用提供了支持。总的来说,我们的工作为将资本结构理论与其经验检验联系起来提供了关键的一步,而不仅仅是简单的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Finance and Data Science
Journal of Finance and Data Science Mathematics-Statistics and Probability
CiteScore
3.90
自引率
0.00%
发文量
15
审稿时长
30 days
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