Paul Goldsmith-Pinkham joined Markus’ Academy for a mini-series on Claude Code for Applied Economists. Goldsmith-Pinkham is an Associate Professor of Finance at the Yale School of Management and a Faculty Research Fellow at NBER.
This post includes the eight episodes of the series. Paul’s detailed notes for each episode are also linked below.
Paul introduced Claude Code as a terminal-based AI coding assistant that can read files, write and run code locally, and accelerate research workflows. He explained his key principles for using these tools optimally, for example the importance of the context window and compaction.
He contrasted Claude Code with the more sandboxed Cowork environment, and discussed other complementary tools like Ghostty, Zellij, and Oh My Zsh.
Paul’s detailed notes on this episode can be found here.
Timestamps:
[1:43] Series Overview
[6:52] What is Claude Code / Cowork?
[22:10] Installation and pricing
[23:54] The context window
[29:31] Privacy
[31:18] Tips for getting started
Paul showed how Claude Code dramatically shrinks the gap between a vague research idea and initial results. As an example, he used Claude Code to obtain and plot data on home ownership in the US. Claude Code found the relevant data from the Census, handled scraping issues, cleaned spreadsheets, generated the required scripts.
Watch the full talk below:
https://www.youtube.com/watch?v=Rp17XUPxa4I
Paul’s detailed notes on this episode can be found here.
Timestamps:
[1:33] Starting from nothing
[6:16] Agent spawning
[13:20] Scraping tips
[22:24] Plotting graphs
[30:04] Results
[35:20] Summing up
Paul used Claude Code to scrape SEC EDGAR filings. Claude Code extracted the risk-factors mentioned in firms’ 10-K reports and built a structured DuckDB database to show how tariff-related risk disclosure became more frequent and more specific in recent years.
Overall, this episode highlighted how to best work with Claude Code by planning and iterating on results. Paul turned messy filings into usable research data in just a few minutes.
Watch the full talk below:
https://www.youtube.com/watch?v=wqLZrKdevHs
Paul’s detailed notes on this episode can be found here.
Timestamps:
[1:26] Developing a plan to scrape 10K filings
[12:40] Modifying the plan
[18:05] Running the scraper and debugging
[24:03] Which sectors mention tariffs the most?
[26:32] Recap
Paul showed how Claude Code can be used to work with large datasets, converting massive CSVs into Parquet, and organizing everything in DuckDB. He also highlighted how Claude Code handles more complex projects: using planning mode, spawning sub-agents, debugging data issues, and managing context.
With Claude Code he built a mortgage market panel from the Home Mortgage Disclosure Act data to study county variation in lender concentration. He extended the pipeline to classify lenders and illustrate the growing share of fintech and non-bank mortgage lending over time.
Watch the full talk below:
Timestamps:
[00:00] Intro
[02:59] Building a mortgage panel from the Home Mortgage Disclosure Act Data
[07:52] DuckDB + Parquet for large-scale data work
[11:47] Claude Code’s planning mode and agent workflow
[27:55] Harmonizing 18 years of mortgage data
[42:01] Fintech lender classification and market-share trends
In three different parts, Paul showed how Claude can support the writing side of research:
First, he showed how to create a personal style guide from your own papers.
Second, he showed how Skills can be used to develop a revision plan after receiving a referee report. He especially recommended Jukka Sihvonen’s Skill for Strategic Revisions.
Third, he shared some advice on how to use LLMs as editors and thinking partners rather than judgment outsourcers.
Watch the full talk below:
https://www.youtube.com/watch?v=BxfSiB3Moyo&t=2579s
Paul’s detailed notes on this episode can be found here.
Timestamps:
[1:27] Developing a Style Guide
[18:12] Using Skills for Referee Reports
[35:37] Claude for Brainstorming and Editing
[43:42] Summary
In this episode, Paul explained Claude Skills: reusable instruction bundles that help Claude perform recurring tasks in a standardized way. He showed where Skills are stored, how global and project-level Skills differ, and then built a paper-summary Skill. Paul also introduced community Skill packs such as “superpowers.”
Watch the full talk below:
https://www.youtube.com/watch?v=a03ehomPqMA&t=162s
Paul’s detailed notes on this episode can be found here.
Timestamps:
[0:00] What are Claude Skills?
[7:33] Building a paper-summary Skill
[21:50] Skill packs, “superpowers,” and cautions about overusing Skills
In this episode, Paul explained how to think about autonomy and risk when using Claude Code and other agentic AI tools, for example covering different types of permissions. Paul showed how containers and sandboxes, including Docker-based workflows and tools like Safe House, can give AI agents more autonomy while limiting potential damage. Paul also discussed OpenClaw-style bots, and introduced us to his own “Duncan Idaho” research assistant agent.
Watch the full talk below:
https://www.youtube.com/watch?v=8Jnx5rL_Gfk&t=2143s
Paul’s detailed notes on this episode can be found here.
Timestamps:
[0:00] Permission types and an overview of agentic tools
[11:37] Sandboxing with containers and Docker
[35:43] OpenClaw and autonomous research agents
Through a replication of Jay Ritter’s results on post-IPO stock performance, Paul shared some best practices for verifying Claude Code’s work. Integrating with GitHub let him track the project as a chain of committed steps rather than one final output, and review Claude’s work one change at a time.
Finally, Paul connected the project’s repository to Overleaf via GitHub (Dropbox works too) so that Claude can update figures and tables directly in one’s paper. The core principle: every number in a draft should come from the code, not from an AI’s memory.
Watch the full talk below:
https://www.youtube.com/watch?v=EcloxLPcRsY&t=3315s
Detailed notes from Paul are available here.
Timestamps:
[0:00] Verification is the new bottleneck
[6:30] Using GitHub to verify Claude’s output
[38:35] How to give feedback
[52:27] Integration with Overleaf for Collaboration
* Hosted by Markus Brunnermeier, with the support of Pablo Balsinde (PhD student, Stockholm School of Economics).
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