Introduction to the Field and Summer School

Key Concepts

International macro finance, micro data, geoeconomics, trade wars, capital misallocation, sovereign debt crisis, safe assets, treasury market, data availability, entry costs, public data (NPORT), commercial data, deep learning, large language models, economic statecraft, graph neural networks, marginal product of capital.

Introduction

Mateo Majori, co-organizer of the summer school on big data in international macro finance, welcomes participants and outlines the program's goals and content. The initiative aims to lower the entry costs for researchers using micro data in the field, providing insights into data availability, issues, and cleaning processes.

The Field of International Macro Finance

• Definition: The field studies the interactions of households, governments, and firms in the global economy.
• Importance: Especially relevant now due to massive changes in the global order.
• Key Challenges:
  • Emergence of China: As a global superpower, impacting both microeconomics and economic statecraft.
  • Eurozone: An economic experiment with 19 countries in a monetary union, facing issues like price adjustment, wage dynamics, sovereign debt crisis, and capital misallocation.
    • Capital misallocation within the Eurozone has been a disappointment, hindering the expected efficiency gains from market integration.
  • US Treasury Market: The US as a provider of safe assets, with rising debt levels and changing composition of debt ownership.
    • Foreign leverage players like hedge funds and mutual funds in Luxembourg and Ireland are significant holders of US securities.
  • Trade Wars: Resurgence of trade wars, bringing international trade and macroeconomics closer together.
    • International macro is catching up to trade due to the increased availability of micro data.
  • Geoeconomics: Countries using trade and financial relationships to exert power.

Why This Initiative?

• Motivation: Started due to the increasing availability of micro data after the financial crisis and European sovereign debt crisis.
• Goal: To lower the entry costs for researchers using micro data.
• Challenges with Micro Data:
  • High costs of acquiring data.
  • Time-consuming data cleaning and processing.
  • Computational power requirements.
• Double Objective:
  1. High-level intellectual content: Why are these questions important?
  2. Nitty-gritty details: Where is the data? What are the issues?
• Value Proposition: Providing insights and cleaning codes to save researchers time and effort.
• Continuous Improvement: The initiative evolves based on student feedback and suggestions.

Program Overview

• Available Resources: Codes, videos, and data sets from previous editions are available on the website.
• Rotating Topics: Core topics are taught every year, with guest lectures on specific areas.
• Faculty:
  • Mateo Majori and Antonio: Teach core topics every year.
  • Jesse Shriger: Co-founder, not present this year.
  • Steve Redding: Trade economist, focusing on data and methods in trade, bridging the gap between macro and trade.
  • Melissa Dell: Expert in artificial intelligence and large language models, presenting her work in the area.
  • Christopher Clayton: Economist, co-author on geoeconomics, focusing on the theoretical underpinnings of measurement.

Detailed Session Breakdown

• Antonio: Aggregate macro data, particularly public data.
  • Emphasizes the value of publicly available micro data for PhD students.
  • Publicly available data is cheap, has no entry cost, and is stable.
• Mateo and Antonio: Micro data, largely commercial data.
  • Discusses how commercial data has allowed for new facts and insights into old questions.
• Steve Redding: Three papers bridging international macro and international trade.
  • Two papers on theory meets data.
  • One paper on geoeconomics, defining economic friendliness between countries.
• Mateo: Public micro data from NPORT (mutual fund position disclosures).
  • Jesse Shriger and Bruno Canavani are leading the effort to clean and benchmark this data.
  • Cleaning codes will be made available.
• Melissa Dell: Deep learning and its application to economics.
  • Using standard economic methodologies to address issues like measurement error bias in deep learning models.
• Christopher Clayton and Mateo: Geoeconomics.
  • Chris will cover the theory behind geoeconomics.
  • Mateo will discuss the use of artificial intelligence and large language models in this area.
• Antonio and Christopher: Using micro data on positions and AI/graph neural networks for central bank applications.
  • Exploring how to extract more information from data using machine learning approaches.

The Field's Potential

• Capital Stock vs. Marginal Product of Capital: Students should focus on areas with high marginal product of capital, not just high capital stock.
• Exciting Time for International Macro Finance:
  • Crucial questions related to the changing world order.
  • Accumulating data.
  • Advancements in data analysis capabilities.
• High Marginal Product: The combination of these factors makes the field highly promising for research.

Conclusion

The summer school aims to equip researchers with the knowledge and tools to effectively use micro data in international macro finance. The field is experiencing a resurgence due to the importance of global issues, the increasing availability of data, and advancements in data analysis techniques. The initiative provides a valuable opportunity to learn from leading experts and contribute to this exciting area of research.