Four monetary rules applied to the Warsh Fed's inheritance do not agree on what it should do: nominal GDP runs 13% above its pre-pandemic path, and the Taylor-rule gap peaked at +8.3 pp in 2022 Q1.

Paper · Data · Site

Note

Runtime: a few minutes, mostly FRED requests. Built with R 4.3 and Python 3.12. Data vintage: published figures use the FRED state at publication; see Data vintage.

R pulls quarterly macroeconomic series from FRED and scores the actual federal funds rate against four monetary-rule benchmarks. Python reads the scored data and generates Chart.js widgets and static figures. The pipeline mirrors the Taylor-rule deep dive repo so the two projects read as one research program.

Quick start

1. Clone: git clone https://github.com/nik110403/warsh-fed.git && cd warsh-fed
2. Get a free FRED API key, then cp .Renviron.example .Renviron and set FRED_API_KEY in it.
3. Run the R pipeline from the repo root: Rscript run_all.R. Packages (fredr, dplyr, zoo, lubridate) install on first run via R/setup.R. The script prints a key-numbers block (latest quarter rates, peak deviations).
4. Install the Python packages (no lock file; any recent versions work): python -m venv .venv && . .venv/bin/activate && pip install numpy pandas matplotlib
5. Build the charts: python python/charts.py and python python/dotplot.py. Figures land in output/figures/ and Chart.js widgets in output/widgets/.

R loads .Renviron from the project root automatically. Run all commands from the repo root; run_all.R errors if called from a subdirectory.

Exhibit map

Every figure in the paper, the script that makes it and the file it writes. Widgets are the interactive Chart.js versions of the same charts.

Exhibit	Script	Output
Headline: Taylor gap and NGDP gap	python/charts.py	output/figures/fig0_hook.png, output/widgets/fig0_hook.html
Policy rate vs. Taylor rules	python/charts.py	output/figures/fig1_taylor_vs_actual.png, output/widgets/fig1_taylor_vs_actual.html
NGDP level vs. 4% path	python/charts.py	output/figures/fig2_ngdp_level.png, output/widgets/fig2_ngdp_level.html
FOMC median dots vs. realized rate	python/dotplot.py	output/figures/fig0_dotgap.png, output/widgets/fig0_dotgap.html

python/charts.py reads output/data/rules_export.csv, which Rscript run_all.R writes. python/dotplot.py needs no pipeline output: the two FOMC median projections and the realized quarterly rate (FRED FEDFUNDS) are hard-coded in the script.

Data availability

Dataset	Source	Access	Included
FEDFUNDS, PCEPILFE, GDPC1, GDPPOT, GDP, M2SL	FRED (BEA, BLS, CBO, Federal Reserve Board)	Public, free API key	Scored output only: output/data/rules_export.csv. Raw pulls (data-raw/) and .rds intermediates are not committed.
FOMC median federal funds rate projections (Dec 2015, Mar 2021)	federalreserve.gov	Public	Hard-coded in python/dotplot.py, together with the realized rate

Source data is served by FRED and subject to its terms. The code licence does not cover it.

Data vintage

FRED revises historical series, including GDPPOT (CBO potential output) and GDP (nominal, subject to benchmark revision). Results reproduce exactly against the vintage retrieved for the published piece; subsequent pulls may shift rule prescriptions. The sample window is locked at 2018 Q1–2026 Q1 in R/config.R (START_DATE, END_DATE).

R/config.R also pins a FRED_VINTAGE (realtime-as-of date) passed as realtime_start/realtime_end on every FRED pull, with the raw response archived under data-raw/fred/<FRED_VINTAGE>/. The published figures predate this pin — they reflect whatever FRED reported at publication time, not FRED_VINTAGE. Re-running R/01_data.R reproduces the FRED state as of FRED_VINTAGE, which will diverge from the published numbers as FRED revises GDPPOT, GDP, and other series.

FRED series

Series ID	Description	Transformation
FEDFUNDS	Effective federal funds rate	levels
PCEPILFE	Core PCE deflator	YoY% (pc1)
GDPC1	Real GDP	levels
GDPPOT	CBO potential real GDP	levels
GDP	Nominal GDP	levels
M2SL	M2 money stock	levels

Monthly series convert to quarterly by taking the last observation of each quarter.

Rule specifications

Rule	Formula	Key parameters
Taylor (baseline)	r* + π + 0.5(π − π*) + 0.5·y_gap	r* = 0.5%, π* = 2%
Taylor (original)	same	r* = 2.0% (Taylor 1993)
Meltzer k-percent	M2 YoY growth vs. fixed target	k = 4%
Selgin productivity norm	NGDP growth vs. potential GDP growth	NGDP trend = 4%/yr from 2019 Q4

Taylor rules are floored at zero. Output gap = 100·(GDPC1 − GDPPOT)/GDPPOT. The NGDP level path compounds at 1% per quarter from the 2019 Q4 base (NGDP_BASE_DATE in R/config.R).

Where things can drift

GDPPOT is revised with each CBO Budget and Economic Outlook, shifting the Taylor output-gap term. GDP benchmark revisions alter both the Meltzer M2 deviation and the NGDP gap. The Meltzer and Selgin rules are more stable to revision than the Taylor variants. The companion piece documents the vintage used.

Repository layout

warsh-fed/
├── run_all.R          # runs the R pipeline in order
├── R/
│   ├── setup.R        # package management
│   ├── config.R       # sample window, vintage, pi*, r*, k-percent, NGDP trend
│   ├── 01_data.R      # FRED pull -> quarterly series -> output/data/df_raw.rds
│   ├── 02_rules.R     # rule scoring -> df_rules.rds
│   └── 03_export.R    # CSV export + key numbers -> output/data/rules_export.csv
├── python/
│   ├── charts.py      # widgets and PNG figures from rules_export.csv
│   └── dotplot.py     # FOMC dots vs. realized rate
├── output/
│   ├── data/          # rules_export.csv
│   ├── figures/       # static PNGs
│   └── widgets/       # Chart.js HTML embeds
├── assets/            # banner and social preview (made by tools/banner)
├── .Renviron.example  # template for the FRED API key
├── CITATION.cff
├── LICENSE            # MIT (code)
└── LICENSE-docs       # CC BY 4.0 (text and figures)

Citation

Archived on Zenodo: doi:10.5281/zenodo.23404663 (all versions). GitHub shows a "Cite this repository" button from CITATION.cff. BibTeX:

BibTeX

@misc{warsh_fed2026,
  author = {Khosravipour, Nik},
  title  = {Inheriting Discretion: What Monetary Rules Prescribe for the Warsh Fed},
  year   = {2026},
  doi    = {10.5281/zenodo.23404663},
  url    = {https://github.com/nik110403/warsh-fed}
}

License

Code: MIT. Text and figures: CC BY 4.0. Source data is public, served by FRED and subject to its terms; neither licence covers it.