Monthly and long-term United States economic indicators data: historical series and analyst forecasts curated by FocusEconomics.

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:/...

This dataset contains historical quarterly data for the U.S. Real Gross Domestic Product, from the first quarter of 1947 to the Q2 2023. Real GDP is an inflation-adjusted measure that reflects the value of all goods and services produced by an economy in a given year, expressed in base-year prices, and is often considered an indicator of a country's standard of living.

The dataset has two columns:

Date: The end of the respective quarter (in MM/DD/0YYYY format). Value: The Real GDP at the end of the respective quarter.

Inspiration: Real GDP is a comprehensive measure of U.S. economic activity and a key tool for economic decision-making and forecasting. Real GDP is used by economists, policy-makers, researchers, and investors to understand the growth and performance of the U.S. economy over time.

Usability: The Real GDP data can be used for a variety of purposes:

Economic Analysis: It can be used for macroeconomic analysis and forecasting. Policy Understanding: It can help understand the impact and effectiveness of economic policies implemented by the U.S. government. Investment Analysis: GDP growth impacts financial markets, and this data can help investors understand and forecast market trends. Education: It can be used in classrooms for teaching economics, finance, and related disciplines.

This collection contains an array of economic time series data pertaining to the United States, the United Kingdom, Germany, and France, primarily between the 1920s and the 1960s, and including some time series from the 18th and 19th centuries. These data were collected by the National Bureau of Economic Research (NBER), and they constitute a research resource of importance to economists as well as to political scientists, sociologists, and historians. Under a grant from the National Science Foundation, ICPSR and the National Bureau of Economic Research converted this collection (which existed heretofore only on handwritten sheets stored in New York) into fully accessible, readily usable, and completely documented machine-readable form. The NBER collection -- containing an estimated 1.6 million entries -- is divided into 16 major categories: (1) construction, (2) prices, (3) security markets, (4) foreign trade, (5) income and employment, (6) financial status of business, (7) volume of transactions, (8) government finance, (9) distribution of commodities, (10) savings and investments, (11) transportation and public utilities, (12) stocks of commodities, (13) interest rates, and (14) indices of leading, coincident, and lagging indicators, (15) money and banking, and (16) production of commodities. Data from all categories are available in Parts 1-22. The economic variables are usually observations on the entire nation or large subsets of the nation. Frequently, however, and especially in the United States, separate regional and metropolitan data are included in other variables. This makes cross-sectional analysis possible in many cases. The time span of variables in these files may be as short as one year or as long as 160 years. Most data pertain to the first half of the 20th century. Many series, however, extend into the 19th century, and a few reach into the 18th. The oldest series, covering brick production in England and Wales, begins in 1785, and the most recent United States data extend to 1968. The unit of analysis is an interval of time -- a year, a quarter, or a month. The bulk of observations are monthly, and most series of monthly data contain annual values or totals.

TThe ERS International Macroeconomic Data Set provides historical and projected data for 181 countries that account for more than 99 percent of the world economy. These data and projections are assembled explicitly to serve as underlying assumptions for the annual USDA agricultural supply and demand projections, which provide a 10-year outlook on U.S. and global agriculture. The macroeconomic projections describe the long-term, 10-year scenario that is used as a benchmark for analyzing the impacts of alternative scenarios and macroeconomic shocks.

Explore the International Macroeconomic Data Set 2015 for annual growth rates, consumer price indices, real GDP per capita, exchange rates, and more. Get detailed projections and forecasts for countries worldwide.

Annual growth rates, Consumer price indices (CPI), Real GDP per capita, Real exchange rates, Population, GDP deflator, Real gross domestic product (GDP), Real GDP shares, GDP, projections, Forecast, Real Estate, Per capita, Deflator, share, Exchange Rates, CPI

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe, WORLD Follow data.kapsarc.org for timely data to advance energy economics research. Notes:

Developed countries/1 Australia, New Zealand, Japan, Other Western Europe, European Union 27, North America

Developed countries less USA/2 Australia, New Zealand, Japan, Other Western Europe, European Union 27, Canada

Developing countries/3 Africa, Middle East, Other Oceania, Asia less Japan, Latin America;

Low-income developing countries/4 Haiti, Afghanistan, Nepal, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Democratic Republic of Congo, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, Somalia, Tanzania, Togo, Uganda, Zimbabwe;

Emerging markets/5 Mexico, Brazil, Chile, Czech Republic, Hungary, Poland, Slovakia, Russia, China, India, Korea, Taiwan, Indonesia, Malaysia, Philippines, Thailand, Vietnam, Singapore

BRIICs/5 Brazil, Russia, India, Indonesia, China; Former Centrally Planned Economies

Former centrally planned economies/7 Cyprus, Malta, Recently acceded countries, Other Central Europe, Former Soviet Union

USMCA/8 Canada, Mexico, United States

Europe and Central Asia/9 Europe, Former Soviet Union

Middle East and North Africa/10 Middle East and North Africa

Other Southeast Asia outlook/11 Malaysia, Philippines, Thailand, Vietnam

Other South America outlook/12 Chile, Colombia, Peru, Bolivia, Paraguay, Uruguay

Indicator Source

Real gross domestic product (GDP) World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service all converted to a 2015 base year.

Real GDP per capita U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table and Population table.

GDP deflator World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Real GDP shares U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table.

Real exchange rates U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, CPI table, and Nominal XR and Trade Weights tables developed by the Economic Research Service.

Consumer price indices (CPI) International Financial Statistics International Monetary Fund, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Population Department of Commerce, Bureau of the Census, U.S. Department of Agriculture, Economic Research Service, International Data Base.

This dataset provides a comprehensive collection of key U.S. macroeconomic indicators spanning the past 25 years (approximately 1998–2023). It includes monthly data on:

M2 Money Supply (M2SL): A broad measure of money in circulation, including cash, checking deposits, and easily convertible near money. Federal Funds Effective Rate (FEDFUNDS): The interest rate at which depository institutions trade federal funds with each other overnight. Interest Rates: Various benchmark interest rates relevant to economic analysis. 10-Year Treasury Constant Maturity Rate (GS10): Reflects market expectations for long-term interest rates and economic growth. All data are sourced from the Federal Reserve Economic Data (FRED) database and are seasonally adjusted where applicable.

This dataset is ideal for economic research, financial modeling, market forecasting, and machine learning applications where macroeconomic variables are relevant. The data is cleaned, merged, and formatted for immediate use, with date-stamped entries aligned on a monthly frequency.

Source: Federal Reserve Economic Data (FRED) — https://fred.stlouisfed.org/

License: CC0: Public Domain

Explore the latest trends in US economic conditions and analyze the performance of a range of industries over the past quarter. Stay informed with our insights.

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?

Delve into the most recent developments in the economic landscape of the United States and assess the performance of various industries during the previous quarter.

Source: UNECE Statistical Database, compiled from national and international (CIS, EUROSTAT, IMF, OECD, World Bank) official sources.General note: The UNECE secretariat presents time series ready for immediate analysis. When appropriate, source segments with methodological differences have been linked and rescaled to build long consistent time series.The national accounts estimates are compiled according to 2008 SNA (System of National Accounts 2008) or 1993 SNA (System of National Accounts 1993).Constant price estimates are based on data compiled by the National Statistical Offices (NSOs), which reflect various national practices (different base years, fixed base, chain, etc.). To facilitate international comparisons, the data reported by the NSOs have been scaled to the current price value of of the common reference year. The resulting chain constant price data are not additive.Common currency (US$) estimates are computed by the secretariat using purchasing power parities (PPPs), which are the rates of currency conversion that equalise the purchasing power of different currencies. PPPs, and not exchange rates, should be used in international comparisons of GDP and its components.Growth rates (per cent) are over the preceding period, unless otherwise specified.Contributions to per cent growth in GDP (in percentage points) are over the preceding period, unless otherwise specified.Regional aggregates are computed by the secretariat. For national accounts all current price aggregates are sums of national series converted into US$ at current PPPs of GDP; all constant price aggregates are calculated by summing up national series scaled to the price level of the common reference year and then converted into US$ using PPPs of GDP of the common reference year. Due to conversion and rounding the resulting aggregates and components could be non-additive.Aggregates are computed for the following regions:UNECE-52:Albania; Armenia; Austria; Azerbaijan; Belarus; Belgium; Bosnia and Herzegovina; Bulgaria; Canada; Croatia; Cyprus; Czech Republic; Denmark; Estonia; Finland; France; Georgia; Germany; Greece; Hungary; Iceland; Ireland; Israel; Italy; Kazakhstan; Kyrgyzstan; Latvia; Lithuania; Luxembourg; Malta; Montenegro; Netherlands; North Macedonia; Norway; Poland; Portugal; Republic of Moldova; Romania; Russian Federation; Serbia; Slovakia; Slovenia; Spain; Sweden; Switzerland; Tajikistan; Turkey; Turkmenistan; Ukraine; United Kingdom; United States; Uzbekistan.North America-2:Canada; United States.European Union-27 (31/12/2020):Austria; Belgium; Bulgaria; Cyprus; Croatia; Czech Republic; Denmark; Estonia; Finland; France; Germany; Greece; Hungary; Ireland; Italy; Latvia; Lithuania; Luxembourg; Malta; Netherlands; Poland; Portugal; Romania; Slovakia; Slovenia; Spain; Sweden.Euro area-20:Austria; Belgium; Croatia; Cyprus; Estonia; Finland; France; Germany; Greece; Ireland; Italy; Latvia; Lithuania; Luxembourg; Malta; Netherlands; Portugal; Slovakia; Slovenia; Spain.Eastern Europe, Caucasus and Central Asia (EECCA):Armenia; Azerbaijan; Belarus; Georgia; Kazakhstan; Kyrgyzstan; Republic of Moldova; Russian Federation; Tajikistan; Turkmenistan; Ukraine; Uzbekistan.CIS-11:Armenia; Azerbaijan; Belarus; Kazakhstan; Kyrgyzstan; Republic of Moldova; Russian Federation; Tajikistan; Turkmenistan; Ukraine; Uzbekistan.Western Balkans-6:Albania; Bosnia and Herzegovina; Croatia; Montenegro; North Macedonia; Serbia... - data not availableThe Coronavirus (COVID-19) pandemic impacts the production of statistics and may limit available resources and data sources. This may impact the quality of statistics for 2020, and could lead to later revisions.

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

Background: Trade openness shows a positive impact on economic growth, supported by economic theory, and export diversification and economic complexity show a positive dynamic in trade openness in the world; however, a specificity is generated in South American countries. Therefore, the objective of the research is to analyse the macroeconomic determinants of trade openness in Latin American countries.

Methods: The research approach was quantitative and explanatory using panel data methodology from the databases of the World Bank, Harvard University and the Economic Commission for Latin America and the Caribbean for the period 2000-2020.

Results: The fixed effects panel data model showed that the variables that had a negative impact on trade openness were GDP, the economic complexity index and the logistic performance index, while the variables that had a positive impact were exports of high-tech products (a proxy for innovation), exports, imports, research and development expenditure and interregional trade in goods.

Conclusions: Therefore, during the analysis period of 2000-2020 in South America, based on the panel data analysis under fixed effects, a total of 8 countries had a negative impact on trade openness, and only the economies of Chile, French Guiana, and Brazil had a positive impact on trade openness; these economies are characterized by their better performance in the economic complexity index, their higher percentage of budget for research and development expenses, and their trade policies oriented towards the industrialization of their value-added products.

There have been more than 700,000 opioid overdose deaths since 2000. To analyze the opioid epidemic, a model is constructed where individuals choose whether to use opioids recreationally, knowing the probabilities of addiction and dying. These odds are functions of recreational opioid usage. The model is fit to estimated Markov chains from the US data that summarize the transitions into and out of opioid addiction as well as to a deadly overdose. The epidemic is broken down into two subperiods: 2000-2010 and 2010–2019. The opioid epidemic's drivers, their impact on employment, and the impact of medical interventions are examined. Lax prescribing practices and misinformation about the risk of addiction are important drivers of the first half of the epidemic. Falling prices for black-market opioids combined with an increase in their lethality are found to be important for the second half.

Recent research by Baker et al. (2013) has created a historical indicator of economic policy uncertainty in the United States, based on an index score derived from content analyses of major U.S. newspapers. Empirical work using this measure has primarily focused on the economic consequences of shifts in economic policy uncertainty. The purpose of this project is to make the first empirical attempt at assessing whether changes in economic policy uncertainty have any role on the tone the President of the United States adopts when speaking about general economic conditions. Using the economic policy uncertainty information devised by Baker et al. (2013), and contrasting this with information about presidential rhetorical tone about the economy developed by Wood (2007), the vector autoregression analysis indicates prior levels of economic policy uncertainty Granger-causes current presidential rhetorical optimism about the economy. The moving average representation analysis suggests that an increase in the economic policy uncertainty index results in a decrease in presidential rhetorical optimism about the general economy.

Both consumer and government spending have continued to rise despite interest rate hikes by the Federal Reserve, but recession fears still loom.

This dataset provides economic indicators for the period 1999-2034, including historical data (1999-2025) and forecasts (2026-2034) generated using the Karfali-VAR Model.

Research Reference:

Title: Extended Research: Karfali-VAR-Model Forecasts and Sensitivity Tests 2026-2034 Author: Jaouad Karfali https://papers.ssrn.com/abstract=5180553 Data Sources BEA: U.S. Bureau of Economic Analysis (GDP Growth) EIA: U.S. Energy Information Administration (Oil Prices) FRED: Federal Reserve Economic Data (S&P 500, Unemployment, Inflation, Interest Rates) Variables Year: Year of observation (1999-2034). Numeric_Cycle: Economic cycle stage (1-9). GDP_Growth (%): Annual GDP growth rate. Oil_Price ($/barrel): Crude oil price per barrel. S&P_500 (Year-End): S&P 500 closing value at the end of the year. Unemployment (%): Annual unemployment rate. Inflation (%): Annual inflation rate. Interest_Rate (%): Central bank interest rate. Usage This dataset can be used for:

Economic forecasting and analysis. Time series modeling and testing. Policy analysis and scenario simulations. License: This dataset is open for research and academic use. Please cite the original SSRN research when using this data.

Summary: Quarterly time series (starting in 1959Q4) of estimates of macroeconomic stars and output gap. These estimates of stars and other model objects were developed using a semi-structural model to jointly estimate “stars” — long-run levels of output (its growth rate), the unemployment rate, the real interest rate, productivity growth, price inflation, and wage inflation. It features links between survey expectations and stars, time-variation in macroeconomic relationships, and stochastic volatility. Survey data help discipline stars’ estimates and have been crucial in estimating a high-dimensional model since the pandemic. The model has desirable real-time properties, competitive forecasting performance, and superior fit to the data compared to variants without the empirical features mentioned above. The paper that developed the model is available from the Working Paper Series of the Federal Reserve Bank of Cleveland - A Unified Framework to Estimate Macroeconomic Stars. For the historical real-time archives: https://github.com/zamansaeed/macrostars/Citation:To learn more about the data and the model, see:Zaman, Saeed. 2024. "A Unified Framework to Estimate Macroeconomic Stars." Working Paper No. 21-23R2. Federal Reserve Bank of Cleveland. https://doi.org/10.26509/frbc-wp-202123r2.JEL CodesC5, E4, E31, E24, O4File Description:Each vintage includes the posterior mean, 68% and 90% Credible Intervals for:U-star: long-run level of unemployment rateR-star: long-run real rate of interestPi-star: long-run level of price inflationP-star: long-run level of productivity growthW-star: long-run level of nominal wage inflationG-star: growth rate of potential outputOutput Gap: cyclical assessment of the US economy Persistence in price inflation (gap)Persistence in nominal wage inflation (gap)Slope of the price Phillips CurveSlope of the wage Phillips CurveShort-run passthrough from prices to wagesWedge: between W-star and (P-star + Pi-star)D: the catch all component in R-star equationStochastic volatility price inflation gapStochastic volatility nominal wage inflation gapStochastic volatility labor productivity gapStochastic volatility interest rate gapStochastic volatility output gapStochastic volatility UR gapDisclaimer:These data are updated by the authors and are not an official product of the Federal Reserve Bank of Cleveland.Latest Estimates of Stars (and the output gap):-- based on US data through 2025Q2In bold is the (posterior) Mean estimate and in parentheses 68% coverage Interval:U-star (long-run level of unemployment rate): 4.4% (4.0% to 4.8%)R-star (long-run real rate of interest): 1.5% (0.8% to 2.2%)Pi-star (long-run level of price inflation): 2.2% (1.7% to 2.6%)P-star (long-run level of productivity growth): 1.7% (1.1% to 2.2%)W-star (long-run level of nominal wage inflation): 3.6% (3.2% to 4.0%)G-star (growth rate of potential output): 2.7% (2.5% to 3.0%)Output Gap (cyclical assessment of the US economy): +0.3% (-0.6% to +1.2%)

Delve into the most recent developments in the economic landscape of the United States and assess the performance of various industries during the previous quarter.

This dataset provides a rich, time-series view of how key macroeconomic indicators have shaped the U.S. housing market over the last 20 years. It is built around the S&P Case-Shiller U.S. National Home Price Index (CSUSHPISA) — a widely trusted benchmark for tracking national home price trends — and enhanced with a curated selection of economic factors sourced from the Federal Reserve Economic Database (FRED).

What's Inside? The dataset spans January 2004 to June 2024 (monthly frequency), and includes the following: Feature Description

Home_Price_Index Case-Shiller Home Price Index (target)
Interest_Rate Federal Funds Rate
Mortgage_Rate 30-Year Fixed Mortgage Average
Unemployment_Rate National unemployment rate
Median_Income Median personal income (annual, forward-filled monthly) Inflation_CPI Consumer Price Index
Building_Permits Housing construction permit approvals
Housing_Starts New housing construction starts
US_Population Monthly estimated population
Consumer_Sentiment University of Michigan Consumer Sentiment Index

In addition to these core features, we’ve added: --Lag features (1-month, 3-month) to capture trend memory --Rolling averages to smooth volatility --Ratios like income-to-mortgage and permit-to-population --Percentage change columns to measure economic shifts over time These transformations make the dataset ideal for predictive modeling, exploratory data analysis, and economic storytelling.

Source --All raw data was retrieved via FRED (Federal Reserve Economic Data), ensuring official, up-to-date, and well-maintained inputs.

Use Cases --Time series forecasting (e.g., Ridge, ARIMA, XGBoost) --Macroeconomic trend analysis --Housing market dashboards --Educational projects on feature engineering --Model interpretability experiments

Frequency --All data is aggregated/resampled to monthly granularity for consistency.

License CC BY 4.0 — free to use with attribution