Welcoming Remarks

At the 5th Annual Conference on "Nontraditional Data, Machine Learning, and Natural Language Processing in Macroeconomics," Washington, D.C.

第五届“宏观经济学中的非传统数据、机器学习和自然语言处理”年会，华盛顿特区

[Right/Left Arrows] seeks the video forwards and back (5 sec );

[右/左箭头] 向前和向后搜索视频（5 秒）；

The [Tab] key may be used in combination with the [Enter/Return] key to navigate and activate control buttons, such as caption on/off.

[Tab] 键可以与 [Enter/Return] 键结合使用来导航和激活控制按钮，例如字幕开/关。

Good morning, and welcome to the 5th annual conference on "Nontraditional Data, Machine Learning, and Natural Language Processing in Macroeconomics." 1 In a world with evolving data sources and significant advances in computing, it is important to constantly improve the conceptual framework that macroeconomic policymakers use to inform their critical decisions.

早上好，欢迎来到第五届“宏观经济学中的非传统数据、机器学习和自然语言处理”年会。 1 在数据源不断发展和计算技术显着进步的世界中，不断改进宏观经济政策制定者用来为其关键决策提供信息的概念框架非常重要。

This conference brings academics and practitioners together to think carefully about that framework and to push for advancements that could lead to improved economic outcomes.

这次会议将学者和从业者聚集在一起，仔细思考该框架，并推动可能改善经济成果的进步。

The Federal Reserve hosted the inaugural edition of this gathering in 2019.

美联储于 2019 年主办了首届会议。

Since then, the conference has evolved into an international partnership between academics, central banks, statistical agencies, and international organizations, with the Bank of Italy, the Bank of Canada, and the Swedish Riksbank hosting recent editions.

从那时起，该会议已发展成为学术界、中央银行、统计机构和国际组织之间的国际合作伙伴关系，意大利银行、加拿大银行和瑞典央行主办了最近几届会议。

The conference is also now jointly organized with the Central Bank Research Association and the Economics with Nontraditional Data and Analytical Tools program, which will further enhance its international scope.

该会议现在还与中央银行研究会和非传统数据和分析工具经济学项目联合举办，这将进一步扩大其国际范围。

We are grateful for the international and domestic partnerships that have emerged from this event and its predecessors.

我们感谢本次活动及其前几届活动所建立的国际和国内伙伴关系。

Today, we have about 90 in-person participants in attendance, representing 30 agencies and foreign central banks.

今天，约有 90 名现场与会者出席，代表 30 个机构和外国央行。

As we continue to leverage new data and master emerging techniques, cooperation between those that provide the statistics, those that base policy upon them, and those that leverage the latest methodologies to understand them will be of paramount importance.

随着我们继续利用新数据并掌握新兴技术，提供统计数据的机构、基于统计数据制定政策的机构以及利用最新方法来理解统计数据的机构之间的合作将变得至关重要。

Coordination between statistical agencies and policymaking institutions helps us achieve our shared goal—a better understanding of the economy.

统计机构和政策制定机构之间的协调有助于我们实现共同目标——更好地了解经济。

From our own institutional perspective, the Federal Reserve uses a broad array of government and private-sector data to continually assess the state of the economy, inform our outlook for economic activity, and evaluate the risks around that outlook in pursuit of our dual mandate of maximum employment and stable prices.

从我们自己的机构角度来看，美联储使用广泛的政府和私营部门数据来持续评估经济状况，告知我们对经济活动的前景，并评估围绕该前景的风险，以实现我们最大就业和稳定价格的双重使命。

Not surprisingly, the emergence of nontraditional, high-frequency data was integral to better understanding the real-time effects of the COVID-19 pandemic and its economic consequences.

毫不奇怪，非传统高频数据的出现对于更好地了解 COVID-19 大流行的实时影响及其经济后果至关重要。

2 In the current environment, we employ a wide variety of tools and techniques to best distill useful signals related to inflation and the labor market from the vast array of information we have at our disposal.

2 在当前环境下，我们采用各种工具和技术，从我们掌握的大量信息中最好地提取与通货膨胀和劳动力市场相关的有用信号。

Of course, that includes the standard econometric toolkit.

当然，这包括标准计量经济学工具包。

But we have also been increasingly relying on text analytics and machine learning.

但我们也越来越依赖文本分析和机器学习。

As a result, this conference is highly relevant for us at the Federal Reserve, and likely those at other central banks as well.

因此，这次会议对于我们美联储以及其他央行来说都非常重要。

More timely and accurate information and improved methodological techniques permit Federal Reserve staff to produce better estimates of the evolving economic outlook, which allows policymakers to make more-informed decisions.

更及时、更准确的信息以及改进的方法技术使美联储工作人员能够对不断变化的经济前景做出更好的估计，从而使政策制定者能够做出更明智的决策。

It is reassuring to know that the use of these nontraditional data and techniques for academic research and for policy is no longer in its infancy.

令人欣慰的是，将这些非传统数据和技术用于学术研究和政策已不再处于起步阶段。

The breadth and depth of studies captured in the agenda display the material inroads of these data and methods into economic research.

议程中涵盖的研究的广度和深度显示了这些数据和方法对经济研究的实质性进展。

I am pleased to see colleagues and important contributors to this field on the conference program.

我很高兴在会议计划中看到该领域的同事和重要贡献者。

Jed Kolko, Julapa Jagtiani, Arthur Turrell, and Hal Varian will discuss issues related to the opportunities and challenges for government and private-sector institutions in response to nontraditional data, machine learning, and artificial intelligence.

Jed Kolko、Julapa Jagtiani、Arthur Turrell 和 Hal Varian 将讨论与政府和私营部门机构应对非传统数据、机器学习和人工智能所面临的机遇和挑战相关的问题。

Similarly, an academic panel with Jesus Fernandez-Villaverde, Sydney Ludvigson, Stephen Hansen, and Chiara Farronato will discuss how these data and methods have helped push the research frontier in subjects ranging from macroeconomic modeling to online markets.

同样，由 Jesus Fernandez-Villaverde、Sydney Ludvigson、Stephen Hansen 和 Chiara Farronato 组成的学术小组将讨论这些数据和方法如何帮助推动从宏观经济模型到在线市场等学科的研究前沿。

As I noted in recent remarks, there has been a surge in excitement and trepidation about generative AI.

正如我在最近的讲话中指出的那样，人们对生成式人工智能的兴奋和恐惧激增。

3 The range of social effects from this new technology could be broad.

3 这项新技术的社会影响范围可能很广泛。

Jack Clark, one of the co-founders of Anthropic, will soon offer a keynote, and I am sure we are all looking forward to hearing his insights on the practical applications of this new AI technology and the potential use cases for economic research and policymaking.

Anthropic 的联合创始人之一杰克·克拉克 (Jack Clark) 很快将发表主题演讲，我相信我们都期待听到他对这种新的人工智能技术的实际应用以及经济研究和政策制定的潜在用例的见解。

I am especially interested in seeing progress on "explainable AI," which can bridge the divide between the technical sphere and users.

我对“可解释的人工智能”的进展特别感兴趣，它可以弥合技术领域和用户之间的鸿沟。

I am looking forward to any discussions on this area.

我期待着关于这一领域的任何讨论。

These generative AI tools could also have implications closer to home, as they may influence how we conduct central bank communications.

这些生成式人工智能工具也可能对家庭产生更深远的影响，因为它们可能会影响我们进行央行沟通的方式。

There is a growing literature that uses natural language processing techniques to discern how communications by central bankers are perceived by the news media, and, in turn, how that influences financial markets.

越来越多的文献使用自然语言处理技术来了解新闻媒体如何看待央行官员的沟通，以及这反过来又如何影响金融市场。

The presentations by Christopher Neely, Clara Vega, Xin Zhang, and Xu Zhang will discuss the state of the art on this subject, which is of material importance to us.

Christopher Neely、Clara Vega、Xin 张和 Xu 张的演讲将讨论这一主题的最新技术，这对我们来说具有重要意义。

Social media has also affected the way we consume news and interact with each other.

社交媒体还影响了我们消费新闻和彼此互动的方式。

This medium is playing an increasingly important role in society and influencing macroeconomic and financial outcomes.

这种媒介在社会中发挥着越来越重要的作用，并影响着宏观经济和金融结果。

As an example, Corbin Fox will present his work with coauthors tomorrow on the effect of social media on the bank runs that took place earlier this year.

例如，科尔宾·福克斯明天将与合著者一起展示他的研究成果，内容涉及社交媒体对今年早些时候发生的银行挤兑的影响。

On the methodological side, machine-learning techniques have also had a profound effect on how we think about modeling complex macroeconomic outcomes.

在方法论方面，机器学习技术也对我们如何思考复杂的宏观经济结果建模产生了深远的影响。

This morning, Philippe Goulet Coulombe will discuss how neural networks help model the volatility of various macroeconomic variables, and Joël Marbet and Yucheng Yang will discuss how new methods are being used to solve models with heterogeneous agents, which are crucial to assess the distributional consequences of economic policies.

今天上午，Philippe Goulet Coulombe 将讨论神经网络如何帮助模拟各种宏观经济变量的波动性，Joël Marbet 和 Yu Cheng Yang 将讨论如何使用新方法来解决异质主体模型，这对于评估经济政策的分配后果至关重要。

Advances in natural language processing and machine learning have also improved our ability to forecast and nowcast a wide range of macroeconomic and financial indicators, a topic that will also be covered in a few sessions tomorrow.

自然语言处理和机器学习的进步也提高了我们预测和即时预测各种宏观经济和金融指标的能力，这个主题也将在明天的几场会议中讨论。

In closing, I hope you have many fruitful and informative conversations in this room and during breaks about the usefulness of nontraditional data and new techniques for macroeconomic analysis.

最后，我希望你们在这个房间和休息时间就非传统数据和宏观经济分析新技术的有用性进行许多富有成果和信息丰富的对话。

I also encourage you to continue your discussions after the conference ends and to seek opportunities for joint work so that we can further develop our understanding of these exciting new tools.

我还鼓励您在会议结束后继续讨论，并寻求共同工作的机会，以便我们能够进一步加深对这些令人兴奋的新工具的理解。

Welcome to the Federal Reserve!

欢迎来到美联储！

I hope you enjoy the conference.

我希望您喜欢这次会议。

1.

1.

The views expressed here are my own and not necessarily those of my colleagues on the Federal Open Market Committee.

这里表达的观点是我自己的观点，不一定是联邦公开市场委员会同事的观点。

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2.

2.

See Tomaz Cajner, Laura J.

参见托马兹·卡伊纳 (Tomaz Cajner)、劳拉·J.

Feiveson, Christopher J.

费弗森，克里斯托弗·J.

Kurz, and Stacey Tevlin (2022), "Use of Nontraditional Data," in Wendy Edelberg, Louise Sheiner, and David Wessel, eds., Recession Remedies: Lessons Learned from the U.S. Economic Policy Response to COVID-19 (Washington: Brookings Institution Press), pp.

Kurz 和 Stacey Tevlin (2022)，“非传统数据的使用”，载于 Wendy Edelberg、Louise Sheiner 和 David Wessel 编辑的《经济衰退补救措施：从美国应对 COVID-19 的经济政策中汲取的经验教训》（华盛顿：布鲁金斯学会出版社），第 173 页。

315–46.

315–46。

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3.

3.

See Lisa D.

参见丽莎 D.

Cook (2023), " Generative AI, Productivity, the Labor Market, and Choice Behavior ," speech delivered at the National Bureau of Economic Research Economics of Artificial Intelligence Conference, Toronto, Canada, September 22.

Cook (2023)，“生成式人工智能、生产力、劳动力市场和选择行为”，9 月 22 日在加拿大多伦多国家经济研究局人工智能经济学会议上发表的演讲。

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