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The natural rate of interest through a hall of mirrors 透过一大厅镜子看到的自然利率
IF 4.1 2区 经济学 Q1 BUSINESS, FINANCE
Journal of Monetary Economics
Pub Date : 2026-01-01 Epub Date: 2025-11-01 DOI: 10.1016/j.jmoneco.2025.103858
Phurichai Rungcharoenkitkul, Fabian Winkler
We propose a novel explanation for persistent movements in the natural rate of interest, or r-star, based on a model of two-sided learning between the central bank and the private sector. Each side has some information about r-star fundamentals and also learns from observing output, inflation and interest rates. When both sides fail to recognise that their actions influence the other’s beliefs, a “hall-of-mirrors” effect arises that causes persistent shifts in r-star in response to cyclical shocks. The model can explain the post-2008 decline in r-star without changes in long-run fundamentals, as well as the excess sensitivity of long-term yields to monetary policy surprises and the underreaction of interest rate forecasts. Aggressive policy easing designed to counter a recession can inadvertently lower r-star and endogenously narrow policy space.
我们基于央行和私营部门之间的双边学习模型，对自然利率（r-star）的持续波动提出了一种新颖的解释。双方都有一些关于r-star基本面的信息，也从观察产出、通货膨胀和利率中学习。当双方都未能认识到自己的行为会影响对方的信念时，就会出现“镜子大厅”效应，导致r-star在周期性冲击下持续变化。该模型可以在不改变长期基本面的情况下解释2008年后r-star的下跌，以及长期收益率对货币政策意外的过度敏感和利率预测的反应不足。旨在应对衰退的激进宽松政策可能会无意中降低r-star，并内在地缩小政策空间。
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引用次数: 0
A theory of fear of floating 害怕漂浮的理论
IF 4.1 2区 经济学 Q1 BUSINESS, FINANCE
Journal of Monetary Economics
Pub Date : 2026-01-01 Epub Date: 2025-11-19 DOI: 10.1016/j.jmoneco.2025.103869
Javier Bianchi , Louphou Coulibaly
Central banks with flexible exchange rate regimes are often reluctant to let their currency float, a phenomenon known as “fear of floating.” We develop a framework in which a floating exchange rate may exacerbate vulnerability to self-fulfilling financial crises rather than provide the intended insulation against external shocks. A commitment to a crawling peg—where the currency can fluctuate within a predetermined band—can help mitigate the risk of self-fulfilling crises. In contrast to the Mundell–Fleming paradigm, the optimal exchange rate policy entails allowing the exchange rate to float in response to real shocks while maintaining it fixed in response to non-fundamental shocks.
实行灵活汇率制度的央行通常不愿让本币浮动，这种现象被称为“浮动恐惧”。我们制定了一个框架，在这个框架中，浮动汇率可能会加剧对自我实现的金融危机的脆弱性，而不是提供预期的抵御外部冲击的隔离。对爬行式盯住汇率的承诺——即人民币可以在预定区间内波动——有助于减轻自我实现危机的风险。与蒙代尔-弗莱明范式相反，最优汇率政策需要允许汇率在应对实际冲击时浮动，而在应对非基本面冲击时保持固定。
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引用次数: 0
Biased surveys 有偏见的调查
IF 4.1 2区 经济学 Q1 BUSINESS, FINANCE
Journal of Monetary Economics
Pub Date : 2026-01-01 Epub Date: 2025-11-14 DOI: 10.1016/j.jmoneco.2025.103868
Luca Gemmi , Rosen Valchev
We find empirical evidence that surveys of professional forecasters are biased by strategic incentives. First, we find that individual forecasts overreact to idiosyncratic information but underreact to common information. We show this is consistent with a model of strategic diversification incentives in forecast reporting where forecasters want to optimally “stand out” from the crowd, and thus report forecasts that exaggerate the agents’ true beliefs. Second, we show that no such biases are present in forecasts data that is not subject to strategic incentives. We also test further comparative statics that also confirm the strategic incentive model. Overall, we conclude that strategic reporting biases the inference an econometrician can draw on the true underlying expectations formation process, and the precision and heterogeneity in agents’ information sets, and lastly we show how to correct for this.
我们发现经验证据表明，对专业预测者的调查受到战略激励的影响。首先，我们发现个体预测对特殊信息反应过度，而对常见信息反应不足。我们表明，这与预测报告中的战略多样化激励模型是一致的，预测者希望从人群中最优地“脱颖而出”，因此报告的预测夸大了代理人的真实信念。其次，我们表明，在不受战略激励影响的预测数据中不存在这种偏差。我们还测试了进一步的比较静态数据，也证实了战略激励模型。总体而言，我们得出的结论是，战略报告偏差是计量经济学家可以借鉴的真实潜在预期形成过程的推断，以及代理人信息集的准确性和异质性，最后我们展示了如何纠正这一点。
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引用次数: 0
The price of intelligence: How should socially-minded firms price and deploy AI? 智能的价格：具有社会意识的公司应该如何定价和部署人工智能？
IF 4.1 2区 经济学 Q1 BUSINESS, FINANCE
Journal of Monetary Economics
Pub Date : 2026-01-01 Epub Date: 2025-12-17 DOI: 10.1016/j.jmoneco.2025.103880
Nils H. Lehr , Pascual Restrepo
Leading AI firms claim to prioritize social welfare. How should firms with a social mandate price and deploy AI? We derive pricing formulas that depart from profit maximization by incorporating incentives to improve welfare and reduce labor disruptions. Using US data, we evaluate several scenarios. A welfarist firm that values both profit and welfare should price closer to marginal cost, as efficiency gains outweigh distributional concerns. A conservative firm focused on labor-market stability should price above the profit-maximizing level in the short run, especially when its AI may displace low-income workers. Overall, socially minded firms face a trade-off between expanding access to AI and the resulting loss in profits and labor market risks.
领先的人工智能公司声称优先考虑社会福利。拥有社会授权的企业应该如何定价和部署人工智能？我们通过纳入提高福利和减少劳动力中断的激励措施，推导出偏离利润最大化的定价公式。使用美国的数据，我们评估了几种情况。一个既重视利润又重视福利的福利主义企业应该更接近边际成本定价，因为效率收益大于分配问题。一家关注劳动力市场稳定的保守公司，短期内的定价应该高于利润最大化水平，尤其是在其人工智能可能取代低收入工人的情况下。总体而言，具有社会意识的公司面临着扩大人工智能使用范围与由此带来的利润损失和劳动力市场风险之间的权衡。
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引用次数: 0
A choice-based approach to the measurement of inflation expectations 一种基于选择的方法来衡量通胀预期
IF 4.1 2区 经济学 Q1 BUSINESS, FINANCE
Journal of Monetary Economics
Pub Date : 2026-01-01 Epub Date: 2025-12-17 DOI: 10.1016/j.jmoneco.2025.103882
Olga Goldfayn-Frank , Pascal Kieren , Stefan Trautmann
Economists widely rely on measures of inflation expectations and uncertainty elicited via density forecasts. This approach, which asks respondents to assign probabilities to pre-specified ranges, has proven highly informative, but also faced criticism in recent periods of elevated and volatile inflation. We propose a new method to elicit the full distribution of inflation expectations, which is rooted in decision theory and can be implemented in standard surveys. In two large surveys and a laboratory experiment, we demonstrate that the proposed method leads to well-defined expectations that fulfil both subjective and objective quality criteria. The method is neither perceived as more difficult nor does it take more time to complete compared to the current standard. In contrast to density forecasts, the method is robust to differences in the state of the economy and thus allows comparisons across time and across countries. The method is portable and can be applied to elicit different macroeconomic expectations.
经济学家普遍依赖通胀预期和密度预测引发的不确定性指标。这种方法要求受访者将概率分配到预先指定的范围内，这种方法已被证明信息量很大，但在最近一段时间内，这种方法也受到了批评。我们提出了一种新的方法来引出通货膨胀预期的完全分布，这种方法植根于决策理论，可以在标准调查中实施。在两个大型调查和一个实验室实验中，我们证明了所提出的方法导致满足主观和客观质量标准的明确定义的期望。与目前的标准相比，该方法既不会被认为更困难，也不会花费更多的时间来完成。与密度预测相比，该方法对经济状况的差异具有很强的稳定期，因此可以进行跨时间和跨国家的比较。该方法是便携式的，可用于引出不同的宏观经济预期。
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引用次数: 0
The impact of AI on global knowledge work 人工智能对全球知识工作的影响
IF 4.1 2区 经济学 Q1 BUSINESS, FINANCE
Journal of Monetary Economics
Pub Date : 2026-01-01 Epub Date: 2025-12-02 DOI: 10.1016/j.jmoneco.2025.103876
Enrique Ide, Eduard Talamàs
We analyze how Artificial Intelligence (AI) reshapes global knowledge work in a two-region world where firms organize production hierarchically to use knowledge efficiently: the most knowledgeable individuals specialize in problem-solving, while others perform routine work. Before AI, the Advanced Economy specializes in problem-solving services, whereas the Emerging Economy focuses on routine work. AI converts compute — which is located in the Advanced Economy — into autonomous “AI agents” that perfectly substitute for humans with a given level of knowledge. Basic AI reduces the Advanced Economy’s net exports of problem-solving services, potentially reversing pre-AI trade patterns. In contrast, sophisticated AI expands these exports, reinforcing existing trade patterns. Finally, we show that a global ban on AI autonomy redistributes AI’s gains toward lower-skilled workers, while a regional ban — such as prohibiting autonomy only in the Emerging Economy — offers little benefit to lower-skilled workers and harms the most knowledgeable individuals in that region.
我们分析了人工智能（AI）如何在两个地区的世界中重塑全球知识工作，在这个世界中，企业按等级组织生产以有效地利用知识：最有知识的个人专门解决问题，而其他人则从事日常工作。在人工智能出现之前，发达经济体专注于解决问题的服务，而新兴经济体则专注于日常工作。人工智能将位于发达经济体的计算机转化为自主的“人工智能代理”，完全替代具有特定知识水平的人类。基础人工智能减少了发达经济体解决问题服务的净出口，有可能逆转人工智能出现之前的贸易模式。相比之下，复杂的人工智能扩大了这些出口，强化了现有的贸易模式。最后，我们表明，对人工智能自治的全球禁令将人工智能的收益重新分配给低技能工人，而区域禁令-例如仅在新兴经济体中禁止自治-对低技能工人几乎没有好处，并且损害了该地区最有知识的个人。
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引用次数: 0
Expectation-driven term structure of equity and bond yields 预期驱动的股票和债券收益率期限结构
IF 4.1 2区 经济学 Q1 BUSINESS, FINANCE
Journal of Monetary Economics
Pub Date : 2026-01-01 Epub Date: 2025-12-16 DOI: 10.1016/j.jmoneco.2025.103881
Ming Zeng , Guihai Zhao
This paper develops a noisy-information equilibrium model to study how subjective expectations shape the joint dynamics of equity and bond yields. In our framework, movements in asset yields are driven by subjective expectations of dividend and GDP growth, rather than time-varying risk premia. A dual-component dividend structure, together with belief distortions, generates key asset-pricing facts: short-term equity yields are more volatile than long-term yields because short-run dividend growth expectations mean-revert to their stable long-run counterpart; the equity yield slope is procyclical due to countercyclical term structure of expected dividend growth; and the bond-stock correlation changes from positive to negative after the late 1990s, reflecting a shift in the correlation between expected GDP and dividend growth. The model also implies predictable dividend strip returns, with predictability declining with maturity due to dividend forecast revisions, and it successfully replicates the observed dynamics of equity yields and some aggregate moments.
本文建立了一个噪声信息均衡模型来研究主观预期如何影响股票和债券收益率的联合动态。在我们的框架中，资产收益率的变动是由股息和GDP增长的主观预期驱动的，而不是随时间变化的风险溢价。双组分股息结构，加上信念扭曲，产生了关键的资产定价事实：短期股票收益率比长期收益率波动性更大，因为短期股息增长预期平均回归到稳定的长期股息增长预期；由于预期股息增长的逆周期期限结构，股票收益率斜率是顺周期的；20世纪90年代末以后，债券与股票的相关性由正变为负，反映了预期GDP与股息增长之间相关性的转变。该模型还暗示了可预测的股息带回报，由于股息预测修正，可预测性随着期限而下降，并且它成功地复制了观察到的股票收益率动态和一些总时刻。
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引用次数: 0
AI and task efficiency 人工智能与任务效率
IF 4.1 2区 经济学 Q1 BUSINESS, FINANCE
Journal of Monetary Economics
Pub Date : 2026-01-01 Epub Date: 2025-12-12 DOI: 10.1016/j.jmoneco.2025.103877
Boyan Jovanovic , Peter L. Rousseau
We model several ways in which AI may improve decisions, raise the productivity of firms, and raise human capital growth. Each focuses on activities that involve problem solving, with solutions being guided by signals. If AI raises the accuracy of the signals, humans will then make better decisions — individually and in groups.
我们对人工智能可能改善决策、提高企业生产率和促进人力资本增长的几种方式进行了建模。每种方法都侧重于解决问题的活动，并由信号指导解决方案。如果人工智能提高了信号的准确性，那么人类就会做出更好的决定——无论是个人还是群体。
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引用次数: 0
Artificial intelligence and cognitive inequality 人工智能和认知不平等
IF 4.1 2区 经济学 Q1 BUSINESS, FINANCE
Journal of Monetary Economics
Pub Date : 2026-01-01 Epub Date: 2025-12-24 DOI: 10.1016/j.jmoneco.2025.103884
Indira Puri , Laura Veldkamp
We combine insights from the medical and artificial intelligence (AI) literatures to propose a novel model, which suggests that the expansion of AI may exacerbate cognitive inequality. Information providers maximize profit by tailoring the complexity of content, offering less cognition-enhancing content to less able customers. While individuals with high cognitive abilities may benefit from this increased within-cognitive-group homogeneity, those with lower cognitive abilities – and even children – may suffer adverse effects. Anecdotal data from political discourse and cognitive skills scores are consistent with the model predictions. The findings introduce a new consideration to the debate on financial literacy and AI regulation.
我们结合医学和人工智能（AI）文献的见解，提出了一个新的模型，该模型表明人工智能的扩张可能会加剧认知不平等。信息提供商通过调整内容的复杂性来最大化利润，向能力较差的客户提供较少的认知增强内容。虽然具有高认知能力的个体可能从这种增加的认知群体内同质性中受益，但那些认知能力较低的人——甚至儿童——可能会受到不利影响。来自政治话语和认知技能得分的轶事数据与模型预测一致。这些发现为有关金融知识和人工智能监管的辩论带来了新的思考。
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引用次数: 0
Is it AI or data that drives firm market power? 是人工智能还是数据驱动了公司的市场力量？
IF 4.1 2区 经济学 Q1 BUSINESS, FINANCE
Journal of Monetary Economics
Pub Date : 2026-01-01 Epub Date: 2025-12-11 DOI: 10.1016/j.jmoneco.2025.103878
Roxana Mihet , Kumar Rishabh , Orlando Gomes
The technology revolution is transforming firm and industry dynamics, yet the roots of firm dominance in the modern economy remain unclear. Is industry dynamism driven by compute capabilities (AI), access to data, or the interaction between them? We develop a dynamic model in which firms gain knowledge from raw data using AI, but face “informational entropy”: without sufficient AI, more raw data leads to information overload and has negative returns. The model has two key predictions: (1) improvements in AI (compute) disproportionately benefit data-rich firms; and (2) access to processed data substitutes for compute, increasing industry dynamism and reducing market concentration. We confirm these predictions using novel data from 2000–2023 and two exogenous shocks: the 2006 launch of Amazon Web Services (AWS) and the 2017 introduction of transformer-based architectures. Our findings suggest that regulating data usability, not just AI models, is essential to preserving competition in the modern economy.
技术革命正在改变企业和行业的动态，但企业在现代经济中占据主导地位的根源仍不清楚。行业动态是由计算能力（AI）、数据访问或它们之间的交互驱动的吗？我们开发了一个动态模型，在这个模型中，企业利用人工智能从原始数据中获取知识，但面临“信息熵”：没有足够的人工智能，更多的原始数据会导致信息过载，并产生负回报。该模型有两个关键预测：(1)人工智能（计算）的改进不成比例地使数据丰富的公司受益；(2)处理后的数据可以替代计算，增加行业活力，降低市场集中度。我们使用2000-2023年的新数据和两个外部冲击来证实这些预测：2006年推出的亚马逊网络服务（AWS）和2017年推出的基于变压器的架构。我们的研究结果表明，规范数据可用性，而不仅仅是人工智能模型，对于保持现代经济中的竞争至关重要。
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引用次数: 0
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