The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

U.S. Economic and Financial Dataset

Dataset Description

This dataset combines historical U.S. economic and financial indicators, spanning the last 50 years, to facilitate time series analysis and uncover patterns in macroeconomic trends. It is designed for exploring relationships between interest rates, inflation, economic growth, stock market performance, and industrial production.

Key Features

• Frequency: Monthly
• Time Period: Last 50 years from Nov-24
• Sources:
  • Federal Reserve Economic Data (FRED)
  • Yahoo Finance

Dataset Feature Description

1. Interest Rate (Interest_Rate):

   • The effective federal funds rate, representing the interest rate at which depository institutions trade federal funds overnight.

2. Inflation (Inflation):

   • The Consumer Price Index for All Urban Consumers, an indicator of inflation trends.

3. GDP (GDP):

   • Real GDP measures the inflation-adjusted value of goods and services produced in the U.S.

4. Unemployment Rate (Unemployment):

   • The percentage of the labor force that is unemployed and actively seeking work.

5. Stock Market Performance (S&P500):

   • Monthly average of the adjusted close price, representing stock market trends.

6. Industrial Production (Ind_Prod):

   • A measure of real output in the industrial sector, including manufacturing, mining, and utilities.

Dataset Statistics

1. Total Entries: 599
2. Columns: 6
3. Memory usage: 37.54 kB
4. Data types: float64

Feature Overview

• Columns:
  • Interest_Rate: Monthly Federal Funds Rate (%)
  • Inflation: CPI (All Urban Consumers, Index)
  • GDP: Real GDP (Billions of Chained 2012 Dollars)
  • Unemployment: Unemployment Rate (%)
  • Ind_Prod: Industrial Production Index (2017=100)
  • S&P500: Monthly Average of S&P 500 Adjusted Close Prices

Executive Summary

This project explores the interconnected dynamics of key macroeconomic indicators and financial market trends over the past 50 years, leveraging data from the Federal Reserve Economic Data (FRED) and Yahoo Finance. The dataset integrates critical variables such as the Federal Funds Rate, Inflation (CPI), Real GDP, Unemployment Rate, Industrial Production, and the S&P 500 Index, providing a holistic view of the U.S. economy and financial markets.

The analysis focuses on uncovering relationships between these variables through time-series visualization, correlation analysis, and trend decomposition. Key findings are included in the Insights section. This project serves as a robust resource for understanding long-term economic trends, policy impacts, and market behavior. It is particularly valuable for students, researchers, policymakers, and financial analysts seeking to connect macroeconomic theory with real-world data.

Potential Use Cases

• Economic Analysis: Examine relationships between interest rates, inflation, GDP, and unemployment.
• Stock Market Prediction: Study how macroeconomic indicators influence stock market trends.
• Time Series Modeling: Perform ARIMA, VAR, or other models to forecast economic trends.
• Cyclic Pattern Analysis: Identify how economic shocks and recoveries impact key indicators.

Snap of Power Analysis

https://github.com/user-attachments/assets/1b40e0ca-7d2e-4fbc-8cfd-df3f09e4fdb8">

To ensure sufficient power, the dataset covers last 50 years of monthly data i.e., around 600 entries.

Key Insights derived through EDA, time-series visualization, correlation analysis, and trend decomposition

• Interest Rate and Inflation Dynamics: The interest Rate and inflation exhibit an inverse relationship, especially during periods of aggressive monetary tightening by the Federal Reserve.
• Economic Growth and Market Performance: GDP growth and the S&P 500 Index show a positive correlation, reflecting how market performance often aligns with overall economic health.
• Labor Market and Industrial Output: Unemployment and industrial production demonstrate a strong inverse relationship. Higher industrial output is typically associated with lower unemployment
• Market Behavior During Economic Shocks: The S&P 500 experienced sharp declines during significant crises, such as the 2008 financial crash and the COVID-19 pandemic in 2020. These events also triggered increased unemployment and contractions in GDP, highlighting the interplay between markets and the broader economy.
• Correlation Highlights: S&P 500 and GDP have a strong positive correlation. Interest rates negatively correlate with GDP and inflation, reflecting monetary policy impacts. Unemployment is negatively correlated with industrial production but positively correlated with interest rates.

Link to GitHub Repo

https:/...

FOMC Meeting Statements & Minutes

This repository automatically scrapes and aggregates the Federal Reserve FOMC meeting statements and minutes - creating a dataset that enables tracking US monetary policy changes through time. It works by polling the website of the U.S. Federal Reserve on a periodic basis and scraping the new statements and minutes as they become available. The scraper runs in a scheduled GitHub Actions workflow, which is available here. The dataset begins in the… See the full description on the dataset page: https://huggingface.co/datasets/vtasca/fomc-statements-minutes.

Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to Aug 2025 about savings, personal, rate, and USA.

Money Supply M2 in the United States increased to 22298.10 USD Billion in October from 22212.50 USD Billion in September of 2025. This dataset provides - United States Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Most consistent estimators are what Müller (2007) terms “highly fragile”: prone to total breakdown in the presence of a handful of unusual data points. This compromises inference. Robust estimation is a (seldom-used) solution, but commonly used methods have drawbacks. In this paper, building on methods that are relatively unknown in economics, we provide a new tool for robust estimates of mean and covariance, useful both for robust estimation and for detection of unusual data points. It is relatively fast and useful for large data sets. Our performance testing indicates that our baseline method performs on par with, or better than, two of the currently best available methods, and that it works well on benchmark data sets. We also demonstrate that the issues we discuss are not merely hypothetical, by re-examining a prominent economic study and demonstrating its central results are driven by a set of unusual points.

Dataset summary for various data sources used in the study.

Call/Call duration data based features.

Context

This dataset includes 1,804 banks nationally ranked on the amount of consolidated assets they hold. The set contains 13 attributes. Ignore banks_dataframe.csv, it has an extra index column.

Content

Attribute Information:

1. Bank Name/Holding Co Name: Name of the bank and the holding company
2. Nat'l Rank: Rank based on amount of consolidated assets
3. Bank ID: ID number the government gives to the bank
4. Bank Location: City and State that the bank is located in
5. Charter: Chartering authority and Federal Reserve membership status
   • NAT=Nationally chartered member bank
   • SMB=State-chartered member bank
   • SNM=State-chartered nonmember bank
6. Consol Asset (Mil $): Consolidated assets
7. Domestic Assets (Mil $): Domestic assets
8. Pct Domestic Assets: Domestic assets as a percentage of consolidated assets
9. Pct Cumulative Assets: Cumulative consolidated assets as a percentage of the sum of consolidated assets for all banks
10. Domestic Branches: Amount of domestic branches the bank has
11. Foreign Branches: Amount of foreign branches the bank has
12. IBF: Bank operates an international banking facility (Yes/No)
13. Pct Foreign Owned: Percentage of foreign ownership

Transaction based features.

Price-To-Tangible-Book-Ratio Time Series for Washington Federal Inc. WaFd, Inc. operates as the bank holding company for Washington Federal Bank that provides lending, depository, insurance, and other banking services in the United States. The company provides deposit products, including business and personal checking accounts, and term certificates of deposit, as well as money market accounts and passbook savings accounts. It offers single-family dwellings, construction, land acquisition and development, consumer lot, multi-family residential, commercial real estate, home equity, business, and consumer loans, as well as commercial and industrial loans. In addition, the company offers insurance brokerage services, such as individual and business insurance policies; holds and markets real estate properties; and debit and credit cards, as well as acts as the trustee. It serves consumers, mid-sized and large businesses, and owners and developers of commercial real estate. The company was formerly known as Washington Federal, Inc. and changed its name to WaFd, Inc. in September 2023. WaFd, Inc. was founded in 1917 and is headquartered in Seattle, Washington.

Testing results—Predicting financial trouble as a function of different feature sets.

Price-To-Tangible-Book-Ratio Time Series for Capitol Federal Financial Inc. Capitol Federal Financial, Inc. operates as the holding company for Capitol Federal Savings Bank that provides various retail banking products and services in the United States. The company accepts a range of deposit products, including savings accounts, money market accounts, interest-bearing and non-interest-bearing checking accounts, and certificates of deposit, as well as retirement accounts. It also provides various loan products, such as one- to four-family residential real estate loans, commercial real estate, commercial and industrial, and construction loans, as well as consumer loans, which include home equity loans and lines of credit, home improvement loans, vehicle loans, and loans secured by savings deposits. In addition, the company offers mobile, telephone, and online banking services, as well as bill payment services and mortgage loan; operates a call center; and invests in various securities. The company serves the metropolitan areas of Topeka, Wichita, Lawrence, Manhattan, Emporia, and Salina, Kansas, and a portion of the metropolitan area of greater Kansas City. Capitol Federal Financial, Inc. was founded in 1893 and is headquartered in Topeka, Kansas.

The 2009 Federal Campus-Based Programs Data Book provides comprehensive program funding information for these federal student aid programs: Federal Supplemental Educational Opportunity Grants, Federal Work-Study, and Federal Perkins Loans. Program allocation data is presented for award year 2009-2010. Fiscal and Recipient data are presented for award year 2007-2008.

This dataset provides information on the unemployment rates for different demographic groups in the United States.

The data is sourced from the Economic Policy Institute’s State of Working America Data Library and economic research conducted by the Federal Reserve Bank of St. Louis.

The dataset contains unemployment rates for various age groups, education levels, genders, races, and more.

Interesting Task Ideas:

1. See how unemployment rates have changed for different groups of people over time.
2. Look into how education levels can affect unemployment rates.
3. Compare unemployment rates between different races / genders.
4. Check out how unemployment rates can vary across different age groups and genders.
5. Find out if there's a connection between education levels and unemployment rates within specific racial or gender groups.
6. Explore how economic downturns can impact unemployment rates for specific groups of people.
7. Use the data to create visuals that show how unemployment rates differ across all sorts of factors.

Don't forget to upvote this dataset if you find it useful! 😊💝

Checkout my other datasets

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Column Descriptions

Definition of data variables

Real output = LN(Gross Domestic Product/ PCE Deflator/ Population) * 100
Real consumption = LN((Personal Consumption Expenditures/ PCE Deflator) / Population) * 100 Real investment = LN((Private Non-Residential Investment/ PCE Deflator) / Population) * 100 Hours worked = LN((Average Weekly Hours * Employment/ 100)/ Population) * 100
Inflation = LN(PCE Deflator / PCE Deflator (-1) ) * 100 Real wage = LN(Hourly Compensation / PCE Deflator) * 100
Policy interest rate = Federal Funds Rate / 4 Relative price of investment = -1 * LN(Price Index of Private Non-Residential Investment/ PCE Deflator) *100

Source of the original data

Gross Domestic Product: Gross Domestic Product, Table 1.1.5. Gross Domestic Product, NIPA Source: U.S. Bureau of Economic Analysis

Personal Consumption Expenditures: Personal Consumption Expenditures, Table 1.1.5. Gross Domestic Product, NIPA Source: U.S. Bureau of Economic Analysis

Private Non-Residential Investment: Private Non-Residential Investment, Table 1.1.5 Gross Domestic Product, NIPA Source: U.S. Bureau of Economic Analysis

PCE Deflator: Personal Consumption Expenditures, Table 1.1.9. Implicit Price Deflator for Gross Domestic Product Source: U.S. Bureau of Economic Analysis

Price Index of Private Non-Residential Investment: Private Non-Residential Capital Formation, Deflator (PIB), OECD Economic Outlook Database Source: Organisation for Economic Co-Operation and Development

Population: Population level, Civilian Noninstitutional Population, 16 Years and Over, Labor Force Statistics from the Current Population Survey, Series ID = LNS10000000 Source: U.S. Bureau of Labor Statistics

(Period: 1947 – 1975) Population: Population level, Civilian Noninstitutional Population, 16 Years and Over, Labor Force Statistics from the Current Population Survey, Series ID = LNU00000000 Source: U.S. Bureau of Labor Statistics

Employment: Employment level, Employed, 16 Years and Over, All Industries, All Occupations, Labor Force Statistics from the Current Population Survey, Series ID = LNS12000000
Source: U.S. Bureau of Labor Statistics

Average Weekly Hours: Average Weekly Hours, Major Sector Productivity and Costs, Nonfarm Business, Series ID = PRS85006023
Source : U.S. Bureau of Labor Statistics

Hourly Compensation: Hourly Compensation, Major Sector Productivity and Costs, Nonfarm Business, Series ID = PRS85006103
Source : U.S. Bureau of Labor Statistics

Federal Funds Rate: Averages of Monthly Figures - Percent
Source: Board of Governors of the Federal Reserve System

This dataset is about book subjects. It has 2 rows and is filtered where the books is Road map for federal acquisition (FAR) reform : a report of the CSIS Working Group on FAR Reform. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Inflation Rate in the United States increased to 3 percent in September from 2.90 percent in August of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Foreign Exchange Reserves in Bangladesh increased to 32335.20 USD Million in October from 31426.80 USD Million in September of 2025. This dataset provides - Bangladesh Foreign Exchange Reserves - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Bill data consumer rating summary.