如何将微数据用于宏观金融

IF 5 3区 经济学 Q1 BUSINESS, FINANCE
D. Sraer, D. Thesmar
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引用次数: 0

摘要

金融摩擦通过限制生产企业获得资本的途径,在企业之间产生资源错配。本文回顾了估计这种分配不当造成的总损失的文献。第一种方法依赖于结构性估计:对具有金融摩擦的企业动态模型进行估计,并用于比较实际经济与具有完美金融市场的反事实经济的效率。我们认为,稳健性是这个结构文献的中心问题。我们讨论了分析结构估计鲁棒性的方法(使用良好识别的矩和分析对矩的敏感性)。第二种方法依赖于充分的统计数据,即使用简单的经验统计来衡量分配不当的公式,并且对一类模型有效。我们将讨论该领域的关键贡献、该方法的普遍性以及该方法有效的条件。充分的统计方法更简单、更可靠,但它们是有代价的:可以探索的反事实分析的范围更有限。《金融经济学年度评论》第15卷的最终在线出版日期预计为2023年11月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How to Use Microdata for Macro-Finance
Financial frictions generate misallocation of resources across firms by restricting productive firms’ access to capital. This article reviews the literature that estimates the aggregate losses generated by such misallocation. The first approach relies on structural estimations: A model of firm dynamics with financial frictions is estimated and used to compare efficiency in the actual economy with a counterfactual with perfect financial markets. We argue that robustness is a central concern for this structural literature. We discuss ways to analyze robustness in structural estimation (using well-identified moments and analyzing sensitivity to moments). The second approach relies on sufficient statistics, namely formulas that measure misallocation using simple empirical statistics and that are valid for a class of models. We discuss key contributions in that space, the generality of this approach, and the conditions under which it is valid. Sufficient statistic approaches are simpler and more robust, but they come at a cost: The range of counterfactual analyses that can be explored is more limited. Expected final online publication date for the Annual Review of Financial Economics, Volume 15 is November 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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CiteScore
5.00
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26
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