Federal Reserve Economic Data

We recently launched a new table that integrates directly with underlying sources FRED aggregates. This new dataset includes a mapping to FRED series ID.


Published by the St. Louis Federal Reserve, FRED aggregates information from various government sources including the US Census Bureau, Bureau of Labor Statistics (BLS), Department of Labor (DOL), Freddie Mac, Department of Transportation (DOT), the US Bureau of Economic Analysis (BEA) and the Federal Reserve. The aggregate data features economic metrics and financial institution specific data such as:

  • CPI

  • Monthly state-level retail sales

  • Foreign exchange rates

  • Industrial production

  • Housing starts

  • Personal Consumption Expenditures (PCE) price index

  • Treasury bill rates

  • Flow of deposits and total liabilities for banks

  • Outstanding balance of commercial real estate loans

  • Nonfarm payroll

Central bank monetary policy rates from Brazil, Canada, England, Mexico, and Japan are also included in FRED tables for ease of comparison with the Federal Funds Effective Rate.

Cybersyn tables that integrate with FRED are only available in our free tier and will be deprecated in the coming months. Any users who rely on these tables are encouraged to transition to the new dataset which includes all data from these tables. Users will be updated about the sunset timeline for the FRED tables via email.

Key Attributes

Geographic Coverage

Global, though US focused

Entity Level

State, Country

Time Granularity

Daily, Weekly, Monthly, Quarterly

Release Frequency

FRED summarizes release calendars from various underlying sources here


Varies by variable and underlying source

As with all Public Domain datasets, Cybersyn aims to release data on Snowflake Marketplace as soon as the underlying source releases new data. We check periodically for changes to the underlying source and, upon detecting a change, propagate the data to Snowflake Marketplace immediately. See our release process for more details.


The FINANCIAL_FRED_ATTRIBUTES table describes each ingested FRED variable:

  • Both the VARIABLE identifier and human-readable VARIABLE_NAME are unique identifiers for a FRED series that detail its unique characteristics.

  • The VARIABLE_GROUP corresponds to the FRED variable name. FRED groups variables based on the primary topic covered. Characteristics of that variable can result in multiple unique variables in our tables (e.g., seasonally adjusted vs. not, multiple reporting frequencies).

  • The RELEASE_SOURCE details where FRED has sourced the data series.

  • The SEASONALLY_ADJUSTED field determines whether a variable has been smoothed out for seasonal fluctuations. Seasonal effects can obscure the true underlying trends in the data, so often a seasonal adjustment is applied to eliminate noise.

  • FREQUENCY is determined by the cadence with which the variable is measured. This is not the variable's release frequency.

  • UNIT describes how the variable is measured. Some variable groups are measured in both number estimates and year-over-year percent change.

  • MEASUREMENT_METHOD details the measurement methodology of the unit of measure.

The FINANCIAL_FRED_TIMESERIES table records the history of each variable:

  • This table is unique by GEO_ID, VARIABLE, and DATE.

  • Variables have varying historical availability.

The FINANCIAL_FRED_VARIABLE_SERIES_ID_CROSSWALK table provides a map between the Cybersyn unique variable identifier and FRED's series ID. This can be useful if looking for a variable in the FRED table by a known FRED Series ID from here.

EAV Model: All Cybersyn products follow the EAV (entity, attributes, value) model with a unified schema. Entities are tangible objects (e.g. geography, company) that Cybersyn provides data on. All timeseries' dates and values that refer to the entity are included in a timeseries table. Descriptors of the timeseries are included in an attributes table. Data is joinable across all Cybersyn products that have a GEO_ID. Refer to Cybersyn Concepts for more details.

Restatements: Timeseries tables, by default, contain the latest published version of all releases. Learn more about point-in-time history tables here.

Source Combination: In some instances, multiple sources of data are combined into a single table. This is true both for aggregator data we propage (ie. FRED) and also for our own aggregatin For example, interest rate data from the Bank of Japan, ECB, etc. live in the financial_fred_timeseries table. Other sources like FFIEC and FDIC are combined to form a unified profile of financial institutions.

Tables & Sources



FRED and various central banks


FRED for historical data, ECB and BIS for daily data

Cybersyn Products

Tables above are available in the following Cybersyn data products:

Examples & Sample Queries

Get Started with FRED Series ID

Use the FINANCIAL_FRED_VARIABLE_SERIES_ID_CROSSWALK table to access timeseries data by FRED series ID.

FROM cybersyn.financial_fred_timeseries ts
INNER JOIN cybersyn.financial_fred_variable_series_id_crosswalk cw
ON ts.geo_id = cw.geo_id AND ts.variable = cw.variable
INNER JOIN cybersyn.financial_fred_attributes att
ON ts.variable = att.variable
INNER JOIN cybersyn.geography_index geo
ON ts.geo_id = geo.geo_id
WHERE series_id = 'GDPC1' -- Real Gross Domestic Product

Aggregate financial data for the banking industry

Calculate the total quarterly net income for the banking industry over the last four decades.

SELECT att.variable_name,,
FROM cybersyn.financial_fred_timeseries AS ts
JOIN cybersyn.financial_fred_attributes AS att ON (ts.variable = att.variable)
WHERE att.variable_name = 'Income and Expense: Net Income (Loss) Attributable to Bank, Not seasonally adjusted, Quarterly, USD'
  AND >= '1984-01-01';

Monitor changes in the Personal Consumption Expenditures (PCE) price index

Track year-over-year changes of the PCE price index for each month since January 2019.

WITH cte AS (
    (value / LAG(value, 12) OVER (PARTITION BY geo_id, variable ORDER BY date) - 1) * 100 AS yoy_change,
FROM cybersyn.financial_fred_timeseries
WHERE variable_name = 'Personal Consumption Expenditures: Chain-type Price Index, Seasonally adjusted, Monthly, Index 2017=100')

FROM cte
WHERE date >= '2019-01-01';

Fed funds rate compared to trimmed mean personal consumption expenditures (PCE)

Timeseries of the effective federal funds effective rate and PCE.

SELECT variable_name,
FROM cybersyn.financial_fred_timeseries
WHERE variable_name IN ('Federal Funds Effective Rate',
                        'Trimmed Mean PCE Inflation Rate, Seasonally adjusted, Monthly, Percent Change YoY')
  AND date >= '2019-01-01'
ORDER BY date;

Evaluate central bank interest rate policies

Compare and contrast interest rate policies for major global economies.

FROM cybersyn.financial_fred_timeseries AS ts
JOIN cybersyn.financial_fred_attributes AS att
    ON (att.variable = ts.variable)
WHERE variable_group IN ('Bank of Brazil Selic Interest Rate Target',
                         'Bank of Canada Overnight Lending Rate',
                         'Bank of England Official Bank Rate',
                         'Bank of Japan Policy-Rate Balance Rate',
                         'Bank of Mexico Official Overnight Target Rate',
                         'ECB Main Refinancing Operations Rate: Fixed Rate Tenders for Euro Area',
                         'Federal Funds Effective Rate');


The data in this product is sourced from St. Louis Federal Reserve, FRED.

Cybersyn is not endorsed by or affiliated with any of these providers. Contact for questions.

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