This page contains Jupyter notebooks that explain how we constructed aggregate investment and Tobin's Q as used in the first two figures of Andrei, Mann, and Moyen (JFE 2019), and how to repeat this process as of 2024 using data available through the FRED API. We hope these files are helpful to those interested in working in this area.

• AMM2019.zip - All files on this page bundled together as a ZIP archive, including the archived data and directory structure needed for the first notebook below, which uses historical archived data.

• AMM2019 - Archived data.ipynb  - This notebook replicates Figures 1 and 2 of the published paper using data that we archived at the time of publication. (PDF version of the notebook output)

  • The algorithm in our paper and in this notebook was designed to follow Hall (2001) as closely as possible, for consistency with prior literature. The sample ends in 2015.

  • There are two minor typos noted in the code, which can be corrected with negligible impact on the results. (PDF of the output with these typos corrected)

• AMM2019 - FRED API data.ipynb -  This notebook implements the same algorithm as the above, but downloading all data from FRED via API, which allows the results to be updated through the most recent available data. (PDF)

  • If you want to run this code, you will of course need to obtain a FRED API key and substitute it into the appropriate line near the start of the notebook.

  • Due to revisions of historical data since the publication of our paper, the data downloaded today will not match the data that we archived.

• AMM2019 - FRED API data - simplified.ipynb - This notebook removes many steps from Hall's algorithm to arrive at a process for constructing Tobin's Q and investment that is much simpler to follow, but yields qualitatively similar conclusions, and is closer in spirit to the way investment-Q regressions are often run in Compustat. See further comments in the file. (PDF)