FRED Economic Data

# FRED Economic Data Access

Overview

Access comprehensive economic data through FRED (Federal Reserve Economic Data), a database maintained by the Federal Reserve Bank of St. Louis containing over 800,000 economic time series from over 100 sources.

• Key capabilities:
• Query economic time series data (GDP, unemployment, inflation, interest rates)
• Search and discover series by keywords, tags, and categories
• Access historical data and vintage (revision) data via ALFRED
• Retrieve release schedules and data publication dates
• Map regional economic data with GeoFRED
• Apply data transformations (percent change, log, etc.)

API Key Setup

Required: All FRED API requests require an API key.

1. Create an account at https://fredaccount.stlouisfed.org
2. Log in and request an API key through the account portal
3. Set as environment variable:

```bash export FRED_API_KEY="your_32_character_key_here" ```

Or in Python: ```python import os os.environ["FRED_API_KEY"] = "your_key_here" ```

Quick Start

Using the FREDQuery Class

```python from scripts.fred_query import FREDQuery

# Initialize with API key fred = FREDQuery(api_key="YOUR_KEY") # or uses FRED_API_KEY env var

# Get GDP data gdp = fred.get_series("GDP") print(f"Latest GDP: {gdp['observations'][-1]}")

# Get unemployment rate observations unemployment = fred.get_observations("UNRATE", limit=12) for obs in unemployment["observations"]: print(f"{obs['date']}: {obs['value']}%")

# Search for inflation series inflation_series = fred.search_series("consumer price index") for s in inflation_series["seriess"][:5]: print(f"{s['id']}: {s['title']}") ```

Direct API Calls

```python import requests import os

API_KEY = os.environ.get("FRED_API_KEY") BASE_URL = "https://api.stlouisfed.org/fred"

# Get series observations response = requests.get( f"{BASE_URL}/series/observations", params={ "api_key": API_KEY, "series_id": "GDP", "file_type": "json" } ) data = response.json() ```

Popular Economic Series

| Series ID | Description | Frequency | |-----------|-------------|-----------| | GDP | Gross Domestic Product | Quarterly | | GDPC1 | Real Gross Domestic Product | Quarterly | | UNRATE | Unemployment Rate | Monthly | | CPIAUCSL | Consumer Price Index (All Urban) | Monthly | | FEDFUNDS | Federal Funds Effective Rate | Monthly | | DGS10 | 10-Year Treasury Constant Maturity | Daily | | HOUST | Housing Starts | Monthly | | PAYEMS | Total Nonfarm Payrolls | Monthly | | INDPRO | Industrial Production Index | Monthly | | M2SL | M2 Money Stock | Monthly | | UMCSENT | Consumer Sentiment | Monthly | | SP500 | S&P 500 | Daily |

API Endpoint Categories

Series Endpoints

Get economic data series metadata and observations.

• Key endpoints:
• `fred/series` - Get series metadata
• `fred/series/observations` - Get data values (most commonly used)
• `fred/series/search` - Search for series by keywords
• `fred/series/updates` - Get recently updated series

```python # Get observations with transformations obs = fred.get_observations( series_id="GDP", units="pch", # percent change frequency="q", # quarterly observation_start="2020-01-01" )

# Search with filters results = fred.search_series( "unemployment", filter_variable="frequency", filter_value="Monthly" ) ```

Reference: See `references/series.md` for all 10 series endpoints

Categories Endpoints

Navigate the hierarchical organization of economic data.

• Key endpoints:
• `fred/category` - Get a category
• `fred/category/children` - Get subcategories
• `fred/category/series` - Get series in a category

```python # Get root categories (category_id=0) root = fred.get_category()

# Get Money Banking & Finance category and its series category = fred.get_category(32991) series = fred.get_category_series(32991) ```

Reference: See `references/categories.md` for all 6 category endpoints

Releases Endpoints

Access data release schedules and publication information.

• Key endpoints:
• `fred/releases` - Get all releases
• `fred/releases/dates` - Get upcoming release dates
• `fred/release/series` - Get series in a release

```python # Get upcoming release dates upcoming = fred.get_release_dates()

# Get GDP release info gdp_release = fred.get_release(53) ```

Reference: See `references/releases.md` for all 9 release endpoints

Tags Endpoints

Discover and filter series using FRED tags.

```python # Find series with multiple tags series = fred.get_series_by_tags(["gdp", "quarterly", "usa"])

# Get related tags related = fred.get_related_tags("inflation") ```

Reference: See `references/tags.md` for all 3 tag endpoints

Sources Endpoints

Get information about data sources (BLS, BEA, Census, etc.).

```python # Get all sources sources = fred.get_sources()

# Get Federal Reserve releases fed_releases = fred.get_source_releases(source_id=1) ```

Reference: See `references/sources.md` for all 3 source endpoints

GeoFRED Endpoints

Access geographic/regional economic data for mapping.

```python # Get state unemployment data regional = fred.get_regional_data( series_group="1220", # Unemployment rate region_type="state", date="2023-01-01", units="Percent", season="NSA" )

# Get GeoJSON shapes shapes = fred.get_shapes("state") ```

Reference: See `references/geofred.md` for all 4 GeoFRED endpoints

Data Transformations

Apply transformations when fetching observations:

| Value | Description | |-------|-------------| | `lin` | Levels (no transformation) | | `chg` | Change from previous period | | `ch1` | Change from year ago | | `pch` | Percent change from previous period | | `pc1` | Percent change from year ago | | `pca` | Compounded annual rate of change | | `cch` | Continuously compounded rate of change | | `cca` | Continuously compounded annual rate of change | | `log` | Natural log |

```python # Get GDP percent change from year ago gdp_growth = fred.get_observations("GDP", units="pc1") ```

Frequency Aggregation

Aggregate data to different frequencies:

| Code | Frequency | |------|-----------| | `d` | Daily | | `w` | Weekly | | `m` | Monthly | | `q` | Quarterly | | `a` | Annual |

Aggregation methods: `avg` (average), `sum`, `eop` (end of period)

```python # Convert daily to monthly average monthly = fred.get_observations( "DGS10", frequency="m", aggregation_method="avg" ) ```

Real-Time (Vintage) Data

Access historical vintages of data via ALFRED:

```python # Get GDP as it was reported on a specific date vintage_gdp = fred.get_observations( "GDP", realtime_start="2020-01-01", realtime_end="2020-01-01" )

# Get all vintage dates for a series vintages = fred.get_vintage_dates("GDP") ```

Common Patterns

Pattern 1: Economic Dashboard

```python def get_economic_snapshot(fred): """Get current values of key indicators.""" indicators = ["GDP", "UNRATE", "CPIAUCSL", "FEDFUNDS", "DGS10"] snapshot = {}

for series_id in indicators: obs = fred.get_observations(series_id, limit=1, sort_order="desc") if obs.get("observations"): latest = obs["observations"][0] snapshot[series_id] = { "value": latest["value"], "date": latest["date"] }

return snapshot ```

Pattern 2: Time Series Comparison

```python def compare_series(fred, series_ids, start_date): """Compare multiple series over time.""" import pandas as pd

data = {} for sid in series_ids: obs = fred.get_observations( sid, observation_start=start_date, units="pc1" # Normalize as percent change ) data[sid] = { o["date"]: float(o["value"]) for o in obs["observations"] if o["value"] != "." }

return pd.DataFrame(data) ```

Pattern 3: Release Calendar

```python def get_upcoming_releases(fred, days=7): """Get data releases in next N days.""" from datetime import datetime, timedelta

end_date = datetime.now() + timedelta(days=days)

releases = fred.get_release_dates( realtime_start=datetime.now().strftime("%Y-%m-%d"), realtime_end=end_date.strftime("%Y-%m-%d"), include_release_dates_with_no_data="true" )

return releases ```

Pattern 4: Regional Analysis

```python def map_state_unemployment(fred, date): """Get unemployment by state for mapping.""" data = fred.get_regional_data( series_group="1220", region_type="state", date=date, units="Percent", frequency="a", season="NSA" )

# Get GeoJSON for mapping shapes = fred.get_shapes("state")

return data, shapes ```

Error Handling

```python result = fred.get_observations("INVALID_SERIES")

if "error" in result: print(f"Error {result['error']['code']}: {result['error']['message']}") elif not result.get("observations"): print("No data available") else: # Process data for obs in result["observations"]: if obs["value"] != ".": # Handle missing values print(f"{obs['date']}: {obs['value']}") ```

Rate Limits

• API implements rate limiting
• HTTP 429 returned when exceeded
• Use caching for frequently accessed data
• The FREDQuery class includes automatic retry with backoff

Reference Documentation

• For detailed endpoint documentation:
• Series endpoints - See `references/series.md`
• Categories endpoints - See `references/categories.md`
• Releases endpoints - See `references/releases.md`
• Tags endpoints - See `references/tags.md`
• Sources endpoints - See `references/sources.md`
• GeoFRED endpoints - See `references/geofred.md`
• API basics - See `references/api_basics.md`

Scripts

`scripts/fred_query.py`

• Main query module with `FREDQuery` class providing:
• Unified interface to all FRED endpoints
• Automatic rate limiting and caching
• Error handling and retry logic
• Type hints and documentation

`scripts/fred_examples.py`

• Comprehensive examples demonstrating:
• Economic indicator retrieval
• Time series analysis
• Release calendar monitoring
• Regional data mapping
• Data transformation and aggregation

Run examples: ```bash uv run python scripts/fred_examples.py ```

Additional Resources

• FRED Homepage: https://fred.stlouisfed.org/
• API Documentation: https://fred.stlouisfed.org/docs/api/fred/
• GeoFRED Maps: https://geofred.stlouisfed.org/
• ALFRED (Vintage Data): https://alfred.stlouisfed.org/
• Terms of Use: https://fred.stlouisfed.org/legal/

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