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On the Persistence of Capital Structure – Reinterpreting What We Know

机译:关于资本结构的持久性-重新解读我们所知道的

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Current literature has suggested that many factors affect a firm's capital structure decision and firms do not change their capital structure very often. Such a "stable" structure has prompted Lemmon, Roberts, and Zender (2007) to advocate the use of firm fixed effect in capital structure regressions. They have shown that firm fixed effect not only explains 60% of cross-sectional variation in leverage, but also crowds out all the known explanatory variables for capital structure. In this paper, we demonstrate that the popular pooled regression ap- proach is less biased than the fixed effect model in explaining the cross-sectional variation in leverage. In other words, the crowding out phenomenon itself actu- ally implies that the existing literature offers useful factors in understanding the capital structures dispersion. This is confirmed in our empirical study, where we find that up to 25% of the variations in the long-term mean can be explained by the known factors. Therefore, given the persistence of capital structure, we argue that it is more important to focus our attention on what determines the dynamics of capital structure. When modeling individual firms' leverage as following an AR(1) process, we find that both persistence and shocks to capital structure are related to a number of firm characteristics. We also find that long-term means and persistence parameters change with business cycles. Our approach to capital structure also allows us to achieve better out-of-sample predictions for leverage.
机译:当前的文献表明,许多因素影响着企业的资本结构决策,企业并不经常改变其资本结构。这种“稳定”的结构促使Lemmon,Roberts和Zender(2007)提倡在资本结构回归中使用公司固定效应。他们已经表明,牢固的固定效应不仅可以解释杠杆横截面变化的60%,而且可以排除所有已知的资本结构解释变量。在本文中,我们证明了在解释杠杆的横截面变化时,流行的集合回归方法比固定效应模型的偏见要小。换句话说,排挤现象本身实际上暗示着现有文献为理解资本结构的分散性提供了有用的因素。这在我们的经验研究中得到了证实,在该研究中,我们发现长期均值中多达25%的变化可以由已知因素来解释。因此,鉴于资本结构的持久性,我们认为将注意力集中在决定资本结构动态的因素上更为重要。当按照AR(1)流程对单个公司的杠杆进行建模时,我们发现资本结构的持久性和冲击都与许多公司特征有关。我们还发现,长期手段和持久性参数会随着业务周期而变化。我们对资本结构的方法还使我们能够实现更好的样本外杠杆预测。

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