一般经济学

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新提交 (展示 2 之 2 条目 )

[1] arXiv:2508.04830 [中文pdf, pdf, 其他]

标题： 联邦储备沟通与新冠疫情 显示英文标题

标题： Federal Reserve Communication and the COVID-19 Pandemic

Jonathan Benchimol, Sophia Kazinnik, Yossi Saadon

期刊参考： 曼彻斯特学派，93(5)，2025，464-484

主题： 一般经济学 (econ.GN) ; 计算与语言 (cs.CL) ; 信息论 (cs.IT) ; 应用 (stat.AP) ; 机器学习 (stat.ML)

在本研究中，我们考察了美联储在新冠疫情期间的沟通策略，并将其与以往经济压力时期的沟通情况进行比较。 使用针对新冠疫情、非常规货币政策（UMP）和金融稳定的专用词典，结合情感分析和主题建模技术，我们识别出美联储在疫情期间的沟通中对金融稳定、市场波动、社会福利和UMP的明显关注，其特点是显著的情境不确定性。 通过比较分析，我们将美联储在新冠疫情危机中的沟通与其在互联网泡沫和全球金融危机中的应对进行对比，研究内容、情感和时间维度。 我们的研究结果表明，美联储的沟通和政策行动对新冠疫情危机的反应比对以往危机更为被动。 此外，利率声明和会议记录中与金融稳定相关的负面情绪预示了随后的宽松货币政策决策。 我们进一步记录到，自全球金融危机以来，讨论UMP已成为美联储公开市场委员会会议纪要和主席演讲的“新常态”，反映了在经济困境时期后沟通策略的机构适应性。 这些发现有助于我们理解中央银行沟通在危机期间如何演变，以及沟通策略如何适应特殊的经济情况。 显示英文摘要

In this study, we examine the Federal Reserve's communication strategies during the COVID-19 pandemic, comparing them with communication during previous periods of economic stress. Using specialized dictionaries tailored to COVID-19, unconventional monetary policy (UMP), and financial stability, combined with sentiment analysis and topic modeling techniques, we identify a distinct focus in Fed communication during the pandemic on financial stability, market volatility, social welfare, and UMP, characterized by notable contextual uncertainty. Through comparative analysis, we juxtapose the Fed's communication during the COVID-19 crisis with its responses during the dot-com and global financial crises, examining content, sentiment, and timing dimensions. Our findings reveal that Fed communication and policy actions were more reactive to the COVID-19 crisis than to previous crises. Additionally, declining sentiment related to financial stability in interest rate announcements and minutes anticipated subsequent accommodative monetary policy decisions. We further document that communicating about UMP has become the "new normal" for the Fed's Federal Open Market Committee meeting minutes and Chairman's speeches since the Global Financial Crisis, reflecting an institutional adaptation in communication strategy following periods of economic distress. These findings contribute to our understanding of how central bank communication evolves during crises and how communication strategies adapt to exceptional economic circumstances.

[2] arXiv:2508.04970 [中文pdf, pdf, html, 其他]

标题： 在统计验证的股票网络中寻找核心平衡模块 显示英文标题

标题： Finding Core Balanced Modules in Statistically Validated Stock Networks

Huan Qing, Xiaofei Xu

主题： 一般经济学 (econ.GN)

基于传统阈值的股票网络存在主观参数选择和固有局限性：它们将关系限制为二进制表示，无法捕捉相关强度和负依赖关系。 为解决这一问题，我们引入了经过统计验证的相关性网络，通过皮尔逊系数的严格t检验仅保留具有统计显著性的相关性。 随后，我们提出了一种新的结构，称为最大强相关平衡模块（LSCBM），其定义为具有结构平衡（即所有三元组的正边积）且具有强成对相关性的最大规模股票组。 这种平衡条件确保了稳定的关系，从而通过负边促进潜在的对冲机会。 理论上，在随机带符号图模型中，我们建立了LSCBM在不同参数条件下渐近存在性、大小缩放和多重性。 为了高效检测LSCBM，我们开发了MaxBalanceCore算法，该算法利用了网络稀疏性。 模拟验证了其效率，展示了在数十秒内扩展到最多10,000个节点的网络的能力。 实证分析表明，LSCBM能够识别出对经济变化和危机做出动态重组的核心市场子系统。 在中国股票市场（2013-2024年），在高压力时期（如2015年崩盘）LSCBM的规模激增，在稳定或碎片化阶段则收缩，而其组成每年在主导行业（如工业和金融）之间轮换。 显示英文摘要

Traditional threshold-based stock networks suffer from subjective parameter selection and inherent limitations: they constrain relationships to binary representations, failing to capture both correlation strength and negative dependencies. To address this, we introduce statistically validated correlation networks that retain only statistically significant correlations via a rigorous t-test of Pearson coefficients. We then propose a novel structure termed the largest strong-correlation balanced module (LSCBM), defined as the maximum-size group of stocks with structural balance (i.e., positive edge-ign products for all triplets) and strong pairwise correlations. This balance condition ensures stable relationships, thus facilitating potential hedging opportunities through negative edges. Theoretically, within a random signed graph model, we establish LSCBM's asymptotic existence, size scaling, and multiplicity under various parameter regimes. To detect LSCBM efficiently, we develop MaxBalanceCore, a heuristic algorithm that leverages network sparsity. Simulations validate its efficiency, demonstrating scalability to networks of up to 10,000 nodes within tens of seconds. Empirical analysis demonstrates that LSCBM identifies core market subsystems that dynamically reorganize in response to economic shifts and crises. In the Chinese stock market (2013-2024), LSCBM's size surges during high-stress periods (e.g., the 2015 crash) and contracts during stable or fragmented regimes, while its composition rotates annually across dominant sectors (e.g., Industrials and Financials).

交叉提交 (展示 1 之 1 条目 )

[3] arXiv:2508.05491 (交叉列表自 cs.CE) [中文pdf, pdf, 其他]

标题： 拆解水晶球：使用 SAISE 框架从临时预测到原理性初创企业评估 显示英文标题

标题： Deconstructing the Crystal Ball: From Ad-Hoc Prediction to Principled Startup Evaluation with the SAISE Framework

Seyed Mohammad Ali Jafari, Ali Mobini Dehkordi, Ehsan Chitsaz, Yadollah Yaghoobzadeh

主题： 计算工程、金融与科学 (cs.CE) ; 一般经济学 (econ.GN)

人工智能（AI）在初创企业评估中的整合代表了一次重大的技术转变，然而支撑这一转变的学术研究在方法论上仍显得支离破碎。 现有的研究通常采用随意的方法，导致研究成果在成功定义、理论特征和严格验证方面存在不一致。 这种碎片化严重限制了当前预测模型的可比性、可靠性和实际应用价值。 为解决这一关键差距，本文对57项实证研究进行了全面的系统文献综述。 我们通过系统地映射定义人工智能驱动的初创企业预测领域的特征、算法、数据来源和评估实践，剖析了当前最先进的状态。 我们的综合分析揭示了一个由核心矛盾定义的领域：对共同工具包——风险投资数据库和基于树的集成方法——有强烈的趋同，但在方法严谨性上却存在显著分歧。 我们识别出四个基础性弱点：对“成功”定义的碎片化，理论指导与数据驱动特征工程之间的分歧，常见与最佳实践模型验证之间的鸿沟，以及在数据伦理和可解释性方面的初步探索。 针对这些发现，我们的主要贡献是提出了系统的人工智能驱动初创企业评估（SAISE）框架。 这个新颖的五阶段指导路线旨在引导研究人员从随意的预测转向有原则的评估。 通过强制实施一种连贯的端到端方法，强调阶段感知的问题定义、理论指导的数据合成、有原则的特征工程、严格的验证和风险意识的解释，SAISE框架为在这个迅速成熟领域内进行更具可比性、稳健和实际相关的研究提供了新的标准。 显示英文摘要

The integration of Artificial Intelligence (AI) into startup evaluation represents a significant technological shift, yet the academic research underpinning this transition remains methodologically fragmented. Existing studies often employ ad-hoc approaches, leading to a body of work with inconsistent definitions of success, atheoretical features, and a lack of rigorous validation. This fragmentation severely limits the comparability, reliability, and practical utility of current predictive models. To address this critical gap, this paper presents a comprehensive systematic literature review of 57 empirical studies. We deconstruct the current state-of-the-art by systematically mapping the features, algorithms, data sources, and evaluation practices that define the AI-driven startup prediction landscape. Our synthesis reveals a field defined by a central paradox: a strong convergence on a common toolkit -- venture databases and tree-based ensembles -- but a stark divergence in methodological rigor. We identify four foundational weaknesses: a fragmented definition of "success," a divide between theory-informed and data-driven feature engineering, a chasm between common and best-practice model validation, and a nascent approach to data ethics and explainability. In response to these findings, our primary contribution is the proposal of the Systematic AI-driven Startup Evaluation (SAISE) Framework. This novel, five-stage prescriptive roadmap is designed to guide researchers from ad-hoc prediction toward principled evaluation. By mandating a coherent, end-to-end methodology that emphasizes stage-aware problem definition, theory-informed data synthesis, principled feature engineering, rigorous validation, and risk-aware interpretation, the SAISE framework provides a new standard for conducting more comparable, robust, and practically relevant research in this rapidly maturing domain

替换提交 (展示 3 之 3 条目 )

[4] arXiv:2403.03649 (替换) [中文pdf, pdf, html, 其他]

标题： 出柜的成本 显示英文标题

标题： The Cost of Coming Out

Enzo Brox, Riccardo Di Francesco

评论： 更新论文的新版本，包含新结果

主题： 一般经济学 (econ.GN)

对社会污名的恐惧使得全球许多个体在披露自己的性取向时犹豫不决。 由于隐藏身份是有成本的，了解反LGB情绪的程度以及对出柜反应的重要性是至关重要的。 本文使用来自一款流行在线游戏的创新数据源，并结合自然实验来克服现有的数据和内生性问题。 我们利用角色身份的外生变化来确定出柜对玩家在全球不同地区对该角色显性偏好的影响。 我们的研究结果揭示了出柜有显著且持续的负面影响。 显示英文摘要

The fear of social stigma leads many individuals worldwide to hesitate in disclosing their sexual orientation. Since concealing identity is costly, it is crucial to understand the extent of anti-LGB sentiments and reactions to coming out. This paper uses an innovative data source from a popular online game together with a natural experiment to overcome existing data and endogeneity issues. We exploit exogenous variation in the identity of a character to identify the effects of coming out on players' revealed preferences for that character across diverse regions globally. Our findings reveal a substantial and persistent negative impact of coming out.

[5] arXiv:2504.16654 (替换) [中文pdf, pdf, html, 其他]

标题： 国际比较：多边指数和非参数福利界限 显示英文标题

标题： International Comparisons: Multilateral Indices and Nonparametric Welfare Bounds

Hubert Wu

主题： 一般经济学 (econ.GN)

多边指数数列，如用于国家间价格和收入比较的指数，是经济学中的基本对象。 然而，由于与偏好误设相关的偏差，这些数列在福利方面的解释可能具有挑战性。 为研究这一问题，我利用参考消费者的概念，并推导出其生活成本指数的显示偏好界限。 这些界限优于传统的对应指标，定义了一个新的指数，并允许对现有指数进行评估。 在一项应用中，我发现超优指数表现优于非超优指数，但我的结果支持许多现代方法的可解释性。 显示英文摘要

Multilateral index numbers, such as those used to make cross-country comparisons of prices and income, are fundamental objects in economics. However, these numbers can be challenging to interpret in terms of welfare due to a bias related to preference misspecification. To study this problem, I exploit the concept of a reference consumer and derive revealed preference bounds on their cost-of-living index. These bounds improve upon their classical counterparts, define a novel index, and permit an appraisal of existing indices. In an application I find that superlative indices outperform non-superlative ones, but my results support the interpretability of many contemporary methods.

[6] arXiv:2507.16078 (替换) [中文pdf, pdf, 其他]

标题： 自动化、人工智能与知识的代际传递 显示英文标题

标题： Automation, AI, and the Intergenerational Transmission of Knowledge

Enrique Ide

主题： 一般经济学 (econ.GN)

人工智能（AI）的最新进展引发了对生产力增长前所未有的期待。 然而，通过使资深员工能够独立完成更多任务，人工智能可能会无意中减少初级职位的机会，引发对未来一代如何获得必要专业知识的担忧。 本文构建了一个模型，以研究先进自动化如何影响知识的代际传递。 分析揭示了一个关键权衡：自动化初级任务可以立即提高生产率，但可能通过削弱年轻工人对隐性技能的获取而危及长期经济增长。 粗略计算表明，由人工智能驱动的初级任务自动化可能会根据自动化的规模降低美国人均产出的长期年增长率0.05至0.35个百分点。 我进一步证明，人工智能副驾驶——提供可扩展访问之前仅通过直接经验获得的隐性类似专业知识的系统——可以通过帮助在职业生涯早期未能发展出足够技能的人来部分缓解这些不利影响。 然而，副驾驶并不总是有益的，因为它们也可能削弱初级员工参与实践学习的动机。 这些发现挑战了人工智能将自动维持生产力增长的乐观观点，相反，强调了保护或积极创造新的初级机会的重要性，以充分释放人工智能的潜力。 显示英文摘要

Recent advances in Artificial Intelligence (AI) have sparked expectations of unprecedented productivity growth. However, by enabling senior workers to accomplish more tasks independently, AI may inadvertently reduce entry-level opportunities, raising concerns about how future generations will acquire essential expertise. This paper develops a model to examine how advanced automation affects the intergenerational transmission of knowledge. The analysis uncovers a critical trade-off: automating entry-level tasks yields immediate productivity gains but risks undermining long-term economic growth by eroding younger workers' acquisition of tacit skills. Back-of-the-envelope calculations suggest that AI-driven entry-level automation could lower the long-run annual growth rate of U.S. per capita output by 0.05 to 0.35 percentage points, depending on the scale of automation. I further demonstrate that AI co-pilots -- systems providing scalable access to tacit-like expertise previously acquired only through direct experience -- can partially mitigate these adverse effects by assisting individuals who fail to develop adequate skills early in their careers. However, co-pilots are not always beneficial, as they may also weaken the incentives of junior workers to engage in hands-on learning. These findings challenge the optimistic view that AI will automatically sustain productivity growth, highlighting instead the importance of safeguarding or actively creating new entry-level opportunities to fully unlock AI's potential.