Content

This series is constructed as Advance Retail and Food Services Sales (https://fred.stlouisfed.org/series/RSAFS) deflated using the Consumer Price Index for All Urban Consumers (1982-84=100) (https://fred.stlouisfed.org/series/CPIAUCSL).

Context

This is a dataset from the Federal Reserve Bank of St. Louis hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1992-01-01

• Observation End : 2019-10-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Ive Erhard on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the Federal Reserve Bank of St. Louis hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Noah Silliman on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Content

This series deflates M1 money stock (https://fred.stlouisfed.org/series/M1SL) with CPI (https://fred.stlouisfed.org/series/CPIAUCSL).

Context

This is a dataset from the Federal Reserve Bank of St. Louis hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1959-01-01

• Observation End : 2019-10-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Precondo CA on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Financial Stress Index (STLFSI2) data was reported at -0.851 % in 07 Jan 2022. This records an increase from the previous number of -0.920 % for 31 Dec 2021. Financial Stress Index (STLFSI2) data is updated weekly, averaging -0.213 % from Dec 1993 (Median) to 07 Jan 2022, with 1463 observations. The data reached an all-time high of 9.193 % in 10 Oct 2008 and a record low of -1.131 % in 22 Oct 2021. Financial Stress Index (STLFSI2) data remains active status in CEIC and is reported by Federal Reserve Bank of St. Louis. The data is categorized under Global Database’s United States – Table US.S018: Financial Stress Index.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

Financial Stress Index (STLFSI3) data was reported at -1.706 % in 28 Oct 2022. This records an increase from the previous number of -1.855 % for 21 Oct 2022. Financial Stress Index (STLFSI3) data is updated weekly, averaging -0.201 % from Dec 1993 (Median) to 28 Oct 2022, with 1505 observations. The data reached an all-time high of 8.257 % in 10 Oct 2008 and a record low of -1.887 % in 12 Aug 2022. Financial Stress Index (STLFSI3) data remains active status in CEIC and is reported by Federal Reserve Bank of St. Louis. The data is categorized under Global Database’s United States – Table US.S018: Financial Stress Index.

Graph and download economic data for All Employees: Leisure and Hospitality: Limited-Service Restaurants and Other Eating Places in California (SMU06000007072259001SA) from Jan 1990 to Jun 2025 about restaurant, leisure, hospitality, food, CA, services, employment, and USA.

Content

Federal Net Outlays as Percent of Gross Domestic Product (FYONGDA188S) was first constructed by the Federal Reserve Bank of St. Louis in January 2013. It is calculated using Federal Net Outlays (FYONET) and Gross Domestic Product (GDPA): FYONGDA188S= ((FYONET/1000)/GDPA)*100 FYONET/1000 transforms FYONET from millions of dollars to billions of dollars.

Context

This is a dataset from the Federal Reserve Bank of St. Louis hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1929-01-01

• Observation End : 2018-01-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Luis Mézquita on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Analysis of ‘Quarterly Real GDP Connecticut’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/5a9fd0fe-8407-4f12-9bcc-11654efba3f7 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

U.S. Bureau of Economic Analysis, Total Real Gross Domestic Product by Industry for Connecticut [CTRQGSP], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CTRQGSP

Units: Millions of Chained 2012 Dollars, Seasonally Adjusted Annual Rate

Updated quarterly.

--- Original source retains full ownership of the source dataset ---

United States Money Supply: MZM data was reported at 15,643.800 USD bn in Nov 2018. This records an increase from the previous number of 15,565.400 USD bn for Oct 2018. United States Money Supply: MZM data is updated monthly, averaging 2,041.500 USD bn from Jan 1959 (Median) to Nov 2018, with 719 observations. The data reached an all-time high of 15,643.800 USD bn in Nov 2018 and a record low of 276.000 USD bn in Feb 1959. United States Money Supply: MZM data remains active status in CEIC and is reported by Federal Reserve Bank of St. Louis. The data is categorized under Global Database’s United States – Table US.KA006: Money, Zero Maturity.

The FHFA House Price Index (FHFA HPI®) is the nation’s only collection of public, freely available house price indexes that measure changes in single-family home values based on data from all 50 states and over 400 American cities that extend back to the mid-1970s. The FHFA HPI incorporates tens of millions of home sales and offers insights about house price fluctuations at the national, census division, state, metro area, county, ZIP code, and census tract levels. FHFA uses a fully transparent methodology based upon a weighted, repeat-sales statistical technique to analyze house price transaction data. ​ What does the FHFA HPI represent? The FHFA HPI is a broad measure of the movement of single-family house prices. The FHFA HPI is a weighted, repeat-sales index, meaning that it measures average price changes in repeat sales or refinancings on the same properties. This information is obtained by reviewing repeat mortgage transactions on single-family properties whose mortgages have been purchased or securitized by Fannie Mae or Freddie Mac since January 1975. The FHFA HPI serves as a timely, accurate indicator of house price trends at various geographic levels. Because of the breadth of the sample, it provides more information than is available in other house price indexes. It also provides housing economists with an improved analytical tool that is useful for estimating changes in the rates of mortgage defaults, prepayments and housing affordability in specific geographic areas. U.S. Federal Housing Finance Agency, All-Transactions House Price Index for Connecticut [CTSTHPI], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CTSTHPI, August 2, 2023.

Analysis of ‘USA Key Economic Indicators’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/calven22/usa-key-macroeconomic-indicators on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Domino’s Pizza, like many other restaurant chains, is getting pinched by higher food costs. The company’s chief executive, Richard Allison, anticipates “unprecedented increases” in the company’s food costs, which could jump by 8-10%. He said that is three to four times what the pizza chain would normally expect in a year.

This leads to the paramount issue of inflation which affects every aspects of the economy, from consumer spending, business investment and employment rates to government programs, tax policies, and interest rates. The recent release of consumer inflation data showed prices rose at the fastest pace since 1982. Inflation forecasting is key in the conduct of monetary policy and can be used in many other ways such as preserving asset values. This dataset is a consolidated macroeconomic official statistics from 1981 to 2021, containing data available in month and quarterly format.

Content

The Core Consumer Price Index (ccpi) measures the changes in the price of goods and services, excluding food and energy due to their volatility. It measures price change from the perspective of the consumer. It is a often used to measure changes in purchasing trends and inflation.

Do note there are some null values in the dataset.

Acknowledgements

All data belongs to the U.S. Bureau of Economic Analysis official release, and are retrieved from FRED, Federal Reserve Bank of St. Louis.

Inspiration

What are some noticeable patterns or seasonality of the economy? What are the current trends of the economy? Which indicators has an effect on Core CPI or vice-versa based on predictive power or influence?

Quarterly data and monthly data can be merged with forward-fill or interpolation methods.

What is the forecast of Core CPI in 2022?

--- Original source retains full ownership of the source dataset ---

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the Federal Reserve Bank of St. Louis hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Francisco Delgado on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

The dataset includes monthly WTI crude oil spot and futures prices with the shortest maturity contracts (one-month, two-month, and three-month futures contracts), the US Ending Stocks of Crude Oil and Petroleum Products in thousands of barrels. All the datasets were sourced from US EIA, except for the three-month US treasury bill dataset sourced from the Federal Reserve Economic Data of St. Louis Federal Bank.

Money Supply M2: Less Small Time Deposits data was reported at 20,855.400 USD bn in Mar 2025. This records an increase from the previous number of 20,542.400 USD bn for Feb 2025. Money Supply M2: Less Small Time Deposits data is updated monthly, averaging 2,369.800 USD bn from Jan 1959 (Median) to Mar 2025, with 795 observations. The data reached an all-time high of 21,815.400 USD bn in Mar 2022 and a record low of 276.000 USD bn in Feb 1959. Money Supply M2: Less Small Time Deposits data remains active status in CEIC and is reported by Federal Reserve Bank of St. Louis. The data is categorized under Global Database’s United States – Table US.KA001: Money Stock Measures.

Explore the Industrial Production Index dataset for the United States, providing valuable insights into the country's industrial output. Access comprehensive information on industrial production trends and performance.

Index, industrial production, USA IPI United StatesFollow data.kapsarc.org for timely data to advance energy economics research..Index 2017=100,Seasonally AdjustedSuggested Citation:Board of Governors of the Federal Reserve System (US), Industrial Production: Total Index [INDPRO], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/INDPRO, February 13, 2023.

Analysis of ‘🚊 Consumer Price Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/consumer-price-indexe on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

9The Consumer Price Index for All Urban Consumers: All Items (CPIAUCSL) is a measure of the average monthly change in the price for goods and services paid by urban consumers between any two time periods.(1) It can also represent the buying habits of urban consumers. This particular index includes roughly 88 percent of the total population, accounting for wage earners, clerical workers, technical workers, self-employed, short-term workers, unemployed, retirees, and those not in the labor force.(1)

The CPIs are based on prices for food, clothing, shelter, and fuels; transportation fares; service fees (e.g., water and sewer service); and sales taxes. Prices are collected monthly from about 4,000 housing units and approximately 26,000 retail establishments across 87 urban areas.(1) To calculate the index, price changes are averaged with weights representing their importance in the spending of the particular group. The index measures price changes (as a percent change) from a predetermined reference date.(1) In addition to the original unadjusted index distributed, the Bureau of Labor Statistics also releases a seasonally adjusted index. The unadjusted series reflects all factors that may influence a change in prices. However, it can be very useful to look at the seasonally adjusted CPI, which removes the effects of seasonal changes, such as weather, school year, production cycles, and holidays.(1)

The CPI can be used to recognize periods of inflation and deflation. Significant increases in the CPI within a short time frame might indicate a period of inflation, and significant decreases in CPI within a short time frame might indicate a period of deflation. However, because the CPI includes volatile food and oil prices, it might not be a reliable measure of inflationary and deflationary periods. For a more accurate detection, the core CPI (Consumer Price Index for All Urban Consumers: All Items Less Food & Energy [CPILFESL]) is often used. When using the CPI, please note that it is not applicable to all consumers and should not be used to determine relative living costs.(1) Additionally, the CPI is a statistical measure vulnerable to sampling error since it is based on a sample of prices and not the complete average.(1)

Attribution: US. Bureau of Labor Statistics from The Federal Reserve Bank of St. Louis

For more information on the consumer price indexes, see:

• (1) Bureau of Economic Analysis. “CPI Detailed Report.” 2013
• (2) Handbook of Methods
• (3) Understanding the CPI: Frequently Asked Questions

This dataset was created by Finance and contains around 900 samples along with Consumer Price Index For All Urban Consumers: All Items, Title:, technical information and other features such as: - Consumer Price Index For All Urban Consumers: All Items - Title: - and more.

How to use this dataset

• Analyze Consumer Price Index For All Urban Consumers: All Items in relation to Title:
• Study the influence of Consumer Price Index For All Urban Consumers: All Items on Title:
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Finance

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--- Original source retains full ownership of the source dataset ---

GDPNow is a nowcasting model for gross domestic product (GDP) growth that synthesizes the bridge equation approach relating GDP subcomponents to monthly source data with factor model and Bayesian vector autoregression approaches. The GDPNow model forecasts GDP growth by aggregating 13 subcomponents that make up GDP with the chain-weighting methodology used by the US Bureau of Economic Analysis.

The Federal Reserve Bank of Atlanta's GDPNow release complements the quarterly GDP release from the Bureau of Economic Analysis (BEA). The Atlanta Fed recalculates and updates their GDPNow forecasts (called "nowcasts") throughout the quarter as new data are released, up until the BEA releases its "advance estimate" of GDP for that quarter. The St. Louis Fed constructs a quarterly time series for this dataset, in which both historical and current observations values are combined. In general, the most-current observation is revised multiple times throughout the quarter. The final forecasted value (before the BEA's release of the advance estimate of GDP) is the static, historical value for that quarter.

For futher information visit the Federal Reserve Bank of Atlanta (https://www.frbatlanta.org/cqer/research/gdpnow.aspx?panel=1).

United States Real-time Sahm Rule Recession Indicator: sa data was reported at 0.270 % Point in Apr 2025. This stayed constant from the previous number of 0.270 % Point for Mar 2025. United States Real-time Sahm Rule Recession Indicator: sa data is updated monthly, averaging 0.070 % Point from Dec 1959 (Median) to Apr 2025, with 785 observations. The data reached an all-time high of 9.500 % Point in Jun 2020 and a record low of -0.370 % Point in Sep 2021. United States Real-time Sahm Rule Recession Indicator: sa data remains active status in CEIC and is reported by Federal Reserve Bank of St. Louis. The data is categorized under Global Database’s United States – Table US.S: Real-time Sahm Rule Recession Indicator.

The dataset presents the global price of coffee for mild arabica varieties, bananas, and sugar per pound, expressed in US dollars for Central America in general. The data have been assessed based on information obtained from the Federal Reserve Bank of St. Louis (FRED).

For more information contact GIS4Tech: info@gis4tech.com. You can also visit the PREDISAN platform https://predisan.gis4tech.com/ca4 for detailed, accurate information.