****Dataset Overview**** This dataset contains historical macroeconomic data, featuring key economic indicators in the United States. It includes important metrics such as the Consumer Price Index (CPI), Retail Sales, Unemployment Rate, Industrial Production, Money Supply (M2), and more. The dataset spans from 1993 to the present and includes monthly data on various economic indicators, processed to show their rate of change (either percentage or absolute difference, depending on the indicator).

provenance

The data in this dataset is sourced from the Federal Reserve Economic Data (FRED) database, hosted by the Federal Reserve Bank of St. Louis. FRED provides access to a wide range of economic data, including key macroeconomic indicators for the United States. My work involved calculating the rate of change (ROC) for each indicator and reorganizing the data into a more usable format for analysis. For more information and access to the full database, visit FRED's website.

Purpose and Use for the Kaggle Community:

This dataset is a valuable resource for data scientists, economists, and analysts interested in understanding macroeconomic trends, performing time series analysis, or building predictive models. With the rate of change included, users can quickly assess the growth or contraction in these indicators month-over-month. This dataset can be used for:

• Exploratory Data Analysis (EDA): Understanding historical economic trends. -Time Series Forecasting: Building models to predict future economic conditions. -Macroeconomic Analysis: Analyzing the relationship between various economic indicators. -Machine Learning Projects: Using the data as features to predict financial or economic outcomes. -By utilizing this dataset, users can perform in-depth analysis on the impact of macroeconomic changes, compare the historical performance of various indicators, and experiment with different time series forecasting techniques.

****Column Descriptions****

Year: The year of the observation.

Month: The month of the observation (1-12).

Industrial Production: Monthly data on the total output of US factories, mines, and utilities.

Manufacturers' New Orders: Durable Goods: Measures the value of new orders placed with manufacturers for durable goods, indicating future production activity.

Consumer Price Index (CPIAUCSL): A measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.

Unemployment Rate: The percentage of the total labor force that is unemployed but actively seeking employment.

Retail Sales: The total receipts of retail stores, indicating consumer spending and economic activity.

Producer Price Index: Measures the average change over time in the selling prices received by domestic producers for their output.

Personal Consumption Expenditures (PCE): A measure of the prices paid by consumers for goods and services, used in calculating inflation.

National Home Price Index: A measure of changes in residential real estate prices across the country.

All Employees, Total Nonfarm: The number of nonfarm payroll employees, an important indicator of the labor market.

Labor Force Participation Rate: The percentage of the working-age population that is either employed or actively looking for work.

Federal Funds Effective Rate: The interest rate at which depository institutions lend reserve balances to other depository institutions overnight.

Building Permits: The number of building permits issued for residential and non-residential buildings, a leading indicator of construction activity.

Money Supply (M2): The total money supply, including cash, checking deposits, and easily convertible near money.

Personal Income: The total income received by individuals from all sources, including wages, investments, and government transfers.

Trade Balance: The difference between a country's imports and exports, indicating the net trade flow.

Consumer Sentiment: The index reflecting consumer sentiment and expectations for the future economic outlook.

Consumer Confidence: A measure of how optimistic or pessimistic consumers are regarding their expected financial situation and the economy.

Notes on Interest Rates Please note that for the Federal Funds Effective Rate (FEDFUNDS), the dataset includes the absolute change in basis points (bps), not the rate of change. This means that the dataset reflects the direct change in the interest rate rather than the percentage change month-over-month. The change is represented in basis points, where 1 basis point equals 0.01%.

This dataset presents key macroeconomic indicators for the State of Qatar on an annual basis. It includes data on GDP, national income, consumption, investment, inflation, trade, government finance, and monetary aggregates. These indicators provide insight into the performance and structure of the Qatari economy across five years.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

IDX Composite: A Leading Indicator of Indonesian Economic Recovery?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Romania's volume index for industrial production decreased by nearly five percent in February 2023 compared to the corresponding month of the previous year. By contrast, the volume index of the services provided to the population marked the highest growth among other macroeconomic indicators, with the growth rate of almost 22 percent year-over-year.

This dataset presents quarterly macroeconomic indicators for the State of Qatar for the years 2022 and 2023. It includes data on GDP, government revenue and expenditure, trade balance, inflation, monetary aggregates, and financial market indicators. Values are provided for each quarter and are disaggregated by key categories such as mining and non-mining sectors, real and nominal growth, and external trade components. The dataset supports economic analysis and policy evaluation by providing timely and detailed insight into the national economic performance.

The Department forecasts a number of macroeconomic aggregates for inclusion in the budget papers. These forecasts have three main purposes: they provide a framework for presenting the economic …Show full descriptionThe Department forecasts a number of macroeconomic aggregates for inclusion in the budget papers. These forecasts have three main purposes: they provide a framework for presenting the economic context in which the budget is developed; most of the economic aggregates or their sub-components are used in the modelling of taxation and other revenue lines; and forecasts of nominal gross state product (GSP) are used to express key financial aggregates in relation to the size of the economy, such as non-financial public sector net debt to nominal GSP. The spreadsheet below contains the seven published aggregates for the budget papers (Budget and Budget Update): real GSP growth; level of nominal GSP; employment growth; unemployment rate; growth in the consumer price index; growth in the wage price index; and annual population growth.

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 ---

These forecasts have three main purposes: •they provide a framework for presenting the economic context in which the budget is developed; •most of the economic aggregates or their sub-components are …Show full descriptionThese forecasts have three main purposes: •they provide a framework for presenting the economic context in which the budget is developed; •most of the economic aggregates or their sub-components are used in the modelling of taxation and other revenue lines; and •forecasts of nominal gross state product (GSP) are used to express key financial aggregates in relation to the size of the economy, such as non-financial public sector net debt to nominal GSP. The spreadsheet below contains the seven published aggregates for the budget papers (Budget and Budget Update): •real GSP growth; •level of nominal GSP; •employment growth; •unemployment rate; •growth in the consumer price index; •growth in the wage price index; and •annual population growth.

CN: Macro-economic Climate Index: Leading Index data was reported at 98.800 2019=100 in Nov 2024. This records an increase from the previous number of 98.700 2019=100 for Oct 2024. CN: Macro-economic Climate Index: Leading Index data is updated monthly, averaging 98.900 2019=100 from Dec 2022 (Median) to Nov 2024, with 24 observations. The data reached an all-time high of 100.680 2019=100 in Apr 2023 and a record low of 95.888 2019=100 in Dec 2022. CN: Macro-economic Climate Index: Leading Index data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Business and Economic Survey – Table CN.OF: Economic Climate Indicator.

DTF forecasts a number of macroeconomic aggregates for inclusion in the budget papers.
These forecasts have three main purposes:

they provide a framework for presenting the economic context in which the budget is developed; most of the economic aggregates or their sub-components are used in the modelling of taxation and other revenue lines; and forecasts of nominal gross state product (GSP) are used to express key financial aggregates in relation to the size of the economy, such as non-financial public sector net debt to nominal GSP.

The spreadsheet below contains the seven published aggregates for the budget papers (Budget and Budget Update):

real GSP growth; level of nominal GSP; employment growth; unemployment rate; growth in the consumer price index; growth in the wage price index; and annual population growth.

These forecasts have three main purposes:\r •they provide a framework for presenting the economic context in which the budget is developed;\r •most of the economic aggregates or their sub-components are used in the modelling of taxation and other revenue lines; and\r •forecasts of nominal gross state product (GSP) are used to express key financial aggregates in relation to the size of the economy, such as non-financial public sector net debt to nominal GSP.\r \r The spreadsheet below contains the seven published aggregates for the budget papers (Budget and Budget Update):\r •real GSP growth;\r •level of nominal GSP;\r •employment growth;\r •unemployment rate;\r •growth in the consumer price index;\r •growth in the wage price index; and\r •annual population growth.\r

This dataset provides a historical summary of Cambodia’s key macroeconomic indicators, covering GDP, inflation, government budget, trade, liquidity, foreign reserves, and demographics. It tracks how the country’s economy evolved from 1993 to 2025p offering a foundational view for economic trend analysis, forecasting, and policymaking.

Blue Chip Financial Forecasts: Monthly surveys of professional economists at leading investment banks, financial firms and consulting firms on the future direction of key U.S. interest rates and other macroeconomic indicators. Forecasts cover each of the next six quarters for each variable such as the Federal Funds Rate, Prime Rate, 3-month LIBOR, and Treasury Bills. Forecasts for interest rates and the Federal Reserve's Major Currency Index represent averages for the quarter, while forecasts for Real GDP, GDP Price Index and Consumer Price Index are seasonally-adjusted annual rates of change. DATA AVAILABLE FOR YEARS: 2001-2020

The Department of Treasury and Finance forecasts a number of macroeconomic aggregates for inclusion in the budget papers.

These forecasts have three main purposes:

• they provide a framework for presenting the economic context in which the budget is developed;
• most of the economic aggregates or their sub-components are used in the modelling of taxation and other revenue lines; and
• forecasts of nominal gross state product (GSP) are used to express key financial aggregates in relation to the size of the economy, such as non-financial public sector net debt to nominal GSP.

The data source contains the seven published aggregates for the budget papers (Budget and Budget Update):

• real GSP growth;
• level of nominal GSP;
• employment growth;
• unemployment rate;
• growth in the consumer price index;
• growth in the wage price index; and
• annual population growth.

DTF forecasts a number of macroeconomic aggregates for inclusion in the budget papers.

These forecasts have three main purposes:

• they provide a framework for presenting the economic context in which the budget is developed;
• most of the economic aggregates or their sub-components are used in the modelling of taxation and other revenue lines; and
• forecasts of nominal gross state product (GSP) are used to express key financial aggregates in relation to the size of the economy, such as non-financial public sector net debt to nominal GSP.

The spreadsheet below contains the seven published aggregates for the budget papers (Budget and Budget Update):

• real GSP growth;
• level of nominal GSP;
• employment growth;
• unemployment rate;
• growth in the consumer price index;
• growth in the wage price index; and
• annual population growth.

These forecasts have three main purposes:\r •they provide a framework for presenting the economic context in which the budget is developed;\r •most of the economic aggregates or their sub-components are used in the modelling of taxation and other revenue lines; and\r •forecasts of nominal gross state product (GSP) are used to express key financial aggregates in relation to the size of the economy, such as non-financial public sector net debt to nominal GSP.\r \r The spreadsheet below contains the seven published aggregates for the budget papers (Budget and Budget Update):\r •real GSP growth;\r •level of nominal GSP;\r •employment growth;\r •unemployment rate;\r •growth in the consumer price index;\r •growth in the wage price index; and\r •annual population growth.\r

Access LSEG's Economic database, featuring global data coverage, consumer confidence data, and macro data indicators.

In 2026, Poland's GDP will grow by ***** percent, inflation will reach *** percent, and the unemployment rate will be *** percent.

Top sectors in 2010-2013, estimates of some macroeconomic indicators for 2013 (Dutch only)

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. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR07644.v2. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats.