Claude Code for Accounting Research
This hands-on PhD course focuses on agentic AI for empirical accounting research. Five modules of hands-on work incorporate AI into the research pipeline responsibly: the course starts at first install and ends at a verified WRDS-to-Stata pipeline, covering reproducible pipelines, text measures from EDGAR, licensed data on your own account, and the verification protocol that makes the speed usable in a paper.
The course closes with a seminar on the economics of knowledge when machines can produce it. For the consumer, AI-created knowledge is privately cheap but socially valuable only if it can be verified and trusted. For the producer, AI lowers the private cost of research, while the social return depends on quality, congestion, and the allocation of scholarly attention. The seminar asks where these private and social margins diverge in the academic labor market, and what remains scarce.