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license: other
license_name: public-domain-source-us-government
pretty_name: "US Macro Data: Recession"
language:
- en
tags:
- finance
- economics
- macroeconomics
- time-series
- fred
- recession
- quant
- backtesting
size_categories:
- 1K<n<10K
configs:
- config_name: long
data_files: data/long.parquet
default: true
- config_name: wide_daily
data_files: data/wide_daily.parquet
- config_name: wide_monthly
data_files: data/wide_monthly.parquet
- config_name: wide_quarterly
data_files: data/wide_quarterly.parquet
---
# US macro data: Recession (13 series)
13 recession series from US government sources, cleaned into one consistent format and ready for pandas.
This free package holds the **last 12 months** (2025-11-04 on) of each series' latest values. It is refreshed weekly.
```python
import pandas as pd
df = pd.read_parquet("hf://datasets/FinzData/macro-recession/data/long.parquet") # series_id, date, value
wide = pd.read_parquet("hf://datasets/FinzData/macro-recession/data/wide_daily.parquet") # one column per series
```
or `datasets.load_dataset("FinzData/macro-recession")`. Wide tables by frequency: `wide_daily` (3 series), `wide_monthly` (7 series), `wide_quarterly` (3 series). `series.csv` lists every series
with its title, units, frequency and how far back the full history goes.
## The full history, point in time, through one API
The FinzData API serves every series back to its first observation, **every revision** (what was known on any past
date, so backtests do not peek at later revisions), and **daily updates**, in the same format:
```python
import finzdata as fz # pip install finzdata
c = fz.Client() # free API key: https://api.finzdata.com/
c.macro("SAHMREALTIME") # full history, latest values
c.macro("SAHMREALTIME", vintage="first") # as first released
c.macro("SAHMREALTIME", as_of="2020-03-01") # exactly what was known on that date
c.macro_series(category="recession") # the catalogue for this category
```
## Most-watched series in this package
| ID | Title | Units | Frequency | Full history from |
|---|---|---|---|---|
| `SAHMREALTIME` | Real-time Sahm Rule Recession Indicator | Percentage Points | Monthly | 1959 |
| `RECPROUSM156N` | Smoothed U.S. Recession Probabilities | Percent | Monthly | 1967 |
| `USREC` | NBER based Recession Indicators for the United States from the Period following the Peak through the Trough | +1 or 0 | Monthly | 1854 |
| `SAHMCURRENT` | Sahm Rule Recession Indicator | Percentage Points | Monthly | 1949 |
| `JHDUSRGDPBR` | Dates of U.S. recessions as inferred by GDP-based recession indicator | +1 or 0 | Quarterly | 1967 |
| `JHGDPBRINDX` | GDP-Based Recession Indicator Index | Percentage Points | Quarterly | 1967 |
| `USRECD` | NBER based Recession Indicators for the United States from the Period following the Peak through the Trough | +1 or 0 | Daily, 7-Day | 1854 |
| `USRECDM` | NBER based Recession Indicators for the United States from the Peak through the Trough | +1 or 0 | Daily, 7-Day | 1854 |
| `USRECQ` | NBER based Recession Indicators for the United States from the Period following the Peak through the Trough | +1 or 0 | Quarterly | 1854 |
| `USRECDP` | NBER based Recession Indicators for the United States from the Peak through the Period preceding the Trough | +1 or 0 | Daily, 7-Day | 1854 |
| `USRECM` | NBER based Recession Indicators for the United States from the Peak through the Trough | +1 or 0 | Monthly | 1854 |
| `RECRISKUSP50` | Recession Probability for the United States, Median Estimate | Percent | Monthly | 1980 |
| `USRECP` | NBER based Recession Indicators for the United States from the Peak through the Period preceding the Trough | +1 or 0 | Monthly | 1854 |
All 13 series: `series.csv`. Browse them at https://finzdata.com/macro/category/recession.
## More from FinzData
- Every macro category: https://huggingface.co/FinzData · index on GitHub: https://github.com/FinzData/macro-data
- Survivorship-free US stock prices since 2000, COT positioning, 13F holdings, insider trades, short interest
- Pricing (history bundles from $195 once): https://finzdata.com/pricing
- Python client (open source): https://github.com/FinzData/finzdata-python
Source: U.S. government agencies via FRED/ALFRED (Federal Reserve Bank of St. Louis). Compiled by FinzData.
Data for research, not investment advice.
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