近距离观察沃什后,大摩首席经济学家坚称:美联储今年不会加息

marsbitPublished on 2026-07-06Last updated on 2026-07-06

Abstract

美联储新任主席沃什的政策立场受到市场密切关注。摩根士丹利首席全球经济学家塞思·卡彭特在参加欧洲央行辛特拉年会后分析指出,综合就业数据、通胀预测及政策信号,美联储今年不会加息。 卡彭特认为,沃什在辛特拉论坛上的表态显示出边际转变:其表述在价格稳定与充分就业双重使命之间更为平衡,并特别指出近期政策会议已推动市场通胀预期和期限溢价下行。这些信号表明美联储并不急于在7月采取行动。 基本面数据支撑这一判断:非农就业为美联储提供了政策缓冲空间,同时大摩的通胀预测低于美联储官员的中值预测。此外,个人消费支出(PCE)通胀指标的方法论修订可能进一步拉低读数。 关于人工智能对货币政策的影响,卡彭特反驳了“AI必然导致通缩并推动降息”的流行观点。他认为,AI投资短期可能边际推升通胀,而更高的生产率增长意味着更高的均衡利率,反而削弱降息逻辑。 相比之下,欧洲央行的政策路径更偏向紧缩。大摩预计欧央行可能在9月再次加息25个基点,但近期偏软的通胀数据和油价下跌为其政策留有变数。

作者:赵颖

美联储新任主席沃什的政策立场正在接受市场的密切审视。摩根士丹利首席全球经济学家Seth Carpenter在出席欧洲央行葡萄牙辛特拉年会后撰文指出,综合就业数据、通胀预测及政策信号,美联储今年不会加息。

Carpenter在报告中写道,沃什在辛特拉政策论坛上的表态延续了其就职发布会的基调——对价格稳定作出有力承诺,但刻意回避了实现这一目标的具体路径。Carpenter注意到两处值得关注的变化:

其一,沃什在双重使命的表述上趋于平衡,从近乎单一聚焦通胀转向更明确地承认充分就业目标;

其二,沃什特别强调,最近一次政策会议(叠加油价下跌)已压低了市场通胀预期和期限溢价,这一表态令Carpenter认为美联储7月加息的可能性不大。

在美联储政策路径存在不确定性的背景下,大摩维持全年不加息的基准预测,意味着市场无需为近期加息风险定价。

沃什的辛特拉信号:平衡双重使命,淡化加息紧迫性

Carpenter亲历了沃什在辛特拉的政策论坛发言,并将其解读为偏鸽派的边际转变。他指出,沃什此前给市场留下的印象是将价格稳定置于压倒性优先地位,而此次发言则更清晰地将充分就业纳入政策框架。

更关键的是,沃什主动点出政策会议已推动市场通胀预期和期限溢价下行,并提及多个"工作组"正在组建、尚需时日。Carpenter认为,这一措辞组合传递出明确信号:美联储并不急于在7月采取行动。

数据支撑耐心:通胀预测低于FOMC中值,非农就业提供缓冲

在基本面层面,Carpenter援引多项因素支持维持不加息预测。上周公布的非农就业数据继续为美联储按兵不动提供空间。与此同时,摩根士丹利的通胀预测明显低于FOMC委员的中位数预测,且PCE通胀的方法论修订存在进一步实质性下调通胀读数的可能性。

Carpenter表示,上述因素叠加,令其对坚持全年不加息的判断感到"合适"(comfortable)。数据当然可能改变结论,但目前的证据指向同一方向。

AI与生产率:不宜简单押注降息

Carpenter还讨论了人工智能对货币政策的影响,并对"AI将带来通缩、推动降息"的流行叙事提出质疑。他指出,AI资本开支的浪潮在美国出现得更早、规模更大,短期内对通胀存在边际推升效应。

更重要的是,他提出三点反驳:第一,商业周期的状态将主导政策走向;第二,通缩效应只是众多影响之一,更高的生产率同样会通过消费和投资拉动需求;第三,更快的生产率增长意味着更高的均衡利率(即经济学家所称的r*),这进一步削弱了降息的逻辑。Carpenter直言,认为AI必然导致降息的简单论断"几乎肯定是错误的"。

欧央行路径分化:9月或再加25基点,但软数据留有变数

与美联储形成对比的是,欧洲央行的政策方向更为明确地偏向紧缩。Carpenter在文中指出,欧央行行长拉加德在辛特拉重申,6月的加息是经过深思熟虑的决定,而非单纯的"预防性加息",这一表述在他看来意味着后续仍有加息空间。

摩根士丹利的基准预测为欧央行将于9月再加息25个基点。不过,Carpenter也指出,上周欧洲通胀数据偏软以及油价大幅下跌为政策留有余地——若通胀持续走软或PMI显著疲弱,再度加息的路径可能受阻。他认为,7月即加息或年内加息超过一次,目前均难以想象。

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Related Questions

Q摩根士丹利首席经济学家Seth Carpenter为何坚称美联储今年不会加息?

A他综合了沃什在辛特拉政策论坛上的鸽派边际转变信号(更平衡的双重使命表述、强调近期会议已压低通胀预期和期限溢价并称需要时间组建工作组)、非农就业数据提供的缓冲空间,以及摩根士丹利自身低于FOMC中值的通胀预测等多重因素,得出了这一判断。

Q美联储新任主席沃什在辛特拉论坛上的表态与之前相比有何主要变化?

A主要变化有两点:其一,在双重使命的表述上更趋于平衡,从近乎单一聚焦通胀转向更明确地承认充分就业目标;其二,主动指出最近的政策会议已推动市场通胀预期和期限溢价下行,并提到多个“工作组”正在组建尚需时日,这被解读为淡化了加息的紧迫性。

Q根据Seth Carpenter的观点,为什么AI(人工智能)的发展不一定导致美联储降息?

A他提出三点反驳:第一,商业周期状态将主导政策;第二,AI的通缩效应只是众多影响之一,而更高的生产率同样会通过增加消费和投资来拉动需求;第三,更快的生产率增长意味着更高的均衡利率(r*),这反而削弱了降息的逻辑。他认为简单将AI与降息挂钩的论断“几乎肯定是错误的”。

Q文章中对欧洲央行和美联储的货币政策路径预期有何不同?

A文章指出两者的政策路径预期出现分化:美联储预计全年不会加息;而欧洲央行则偏向紧缩,摩根士丹利的基准预测是欧央行将在9月再加息25个基点,尽管软数据和油价下跌为其政策留有变数。

QSeth Carpenter认为哪些数据因素支持美联储今年“按兵不动”的预测?

A他认为支持因素包括:非农就业数据为美联储提供了缓冲空间;摩根士丹利的通胀预测明显低于美联储公开市场委员会(FOMC)委员的中位数预测;此外,个人消费支出(PCE)通胀的方法论修订有可能进一步实质性地压低通胀读数。

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Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

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