In the third quarter of 2024, half of Norwegian companies had problems with increasing purchase prices as a result of rising inflation seen around the world. Moreover, more than 40 percent faced problems due to an unstable economic framework. On the other hand, only 10 percent had issues with lack of credits or financing. As a consequence of the COVID-19 pandemic, as well as the Russian War in Ukraine that started in February 2022, inflation has been surging worldwide. For more information about inflation in the Nordic countries, please visit our dedicated topic page.

CNBC Economy Articles Dataset is an invaluable collection of data extracted from CNBC’s economy section, offering deep insights into global and U.S. economic trends, market dynamics, financial policies, and industry developments.

This dataset encompasses a diverse array of economic articles on critical topics like GDP growth, inflation rates, employment statistics, central bank policies, and major global events influencing the market. Designed for researchers, analysts, and businesses, it serves as an essential resource for understanding economic patterns, conducting sentiment analysis, and developing financial forecasting models.

Dataset Highlights

Each record in the dataset is meticulously structured and includes:

• Article Titles
• Publication Dates
• Author Names
• Content Summaries
• URLs to Original Articles

This rich combination of fields ensures seamless integration into data science projects, research papers, and market analyses.

Key Features

• Number of Articles: Hundreds of articles sourced directly from CNBC.
• Data Fields: Includes title, publication date, author, article content, summary, URL, and relevant keywords.
• Topics Covered: U.S. and global economy, GDP trends, inflation, employment, financial markets, and monetary policies.
• Format: Delivered in CSV format for easy integration with research tools and analytical platforms.
• Source: Extracted directly from CNBC’s economy news section, ensuring accuracy and relevance.

Use Cases

• Economic Research: Gain insights into U.S. and global economic policies, market trends, and industry developments.
• Sentiment Analysis: Assess the sentiment of economic articles to gauge market perspectives and investor confidence.
• Financial Modeling: Build forecasting models leveraging key economic indicators discussed in the dataset.
• Content Creation: Develop research-backed reports, articles, and presentations on economic topics.

Who Benefits?

• Researchers & Academics studying macro-economics or financial policy.
  
• Data Scientists building AI models, trend analyzers, or economic forecasting tools.
  
• Economists & Analysts need real-world news data for policy analysis.
  
• Content Strategists who write data-backed articles about economic trends.

Why Choose This Dataset?

• No need to manually scrape CNBC — data is pre-extracted and clean.
  
• High-quality economy news metadata enables detailed filtering (by date, author, topic).
  
• Ready for machine learning, sentiment analysis, or building news-based economic models.
  
• Well-suited for trend tracking, policy analysis, and economic forecasting.

Explore More News Datasets

Interested in additional structured news datasets for your research or analytics needs? Check out our news dataset collection to find datasets tailored for diverse analytical applications.

This research focuses on the longer-term monetary relationships in historical data. Charts describing the 10-year average growth rates in the M2 monetary aggregate, nominal GDP, real GDP, and inflation are used to show that there is a consistent longer-term correlation between M2 growth, nominal GDP growth, and inflation but not between such nominal variables and real GDP growth. The data reveal extremely long cycles in monetary growth and inflation, the most recent of which was the strong upward trend in M2 growth, nominal GDP growth, and inflation during the 1960s and 1970s, and the strong downward trend since then. Data going back to the 19th century show that the most recent inflation/disinflation cycle is a repetition of earlier long monetary growth and inflation cycles in the United States historical record. Also discussed is a measure of bond market inflation credibility, defined as the difference between averages in long-term bond rates and real GDP growth. By this measure, inflation credibility hovered close to zero during the 1950s and early 1960s, but then rose to a peak of about 10 percent in the early 1980s. During the 1990s, the bond market has yet to restore the low inflation credibility that existed before inflation turned up during the 1960s. The conclusion is that the risks of starting another costly inflation/disinflation cycle could be avoided by monitoring monetary growth and maintaining a sufficiently tight policy to keep inflation low. An environment of credible price stability would allow the economy to function unfettered by inflationary distortions, which is all that can reasonably be expected of monetary policy, and is precisely what should be expected.

During the period beginning roughly in the mid-1980s until the Global Financial Crisis (2007-2008), the U.S. economy experienced a time of relative economic calm, with low inflation and consistent GDP growth. Compared with the turbulent economic era which had preceded it in the 1970s and the early 1980s, the lack of extreme fluctuations in the business cycle led some commentators to suggest that macroeconomic issues such as high inflation, long-term unemployment and financial crises were a thing of the past. Indeed, the President of the American Economic Association, Professor Robert Lucas, famously proclaimed in 2003 that "central problem of depression prevention has been solved, for all practical purposes". Ben Bernanke, the future chairman of the Federal Reserve during the Global Financial Crisis (GFC) and 2022 Nobel Prize in Economics recipient, coined the term 'the Great Moderation' to describe this era of newfound economic confidence. The era came to an abrupt end with the outbreak of the GFC in the Summer of 2007, as the U.S. financial system began to crash due to a downturn in the real estate market.

Causes of the Great Moderation, and its downfall

A number of factors have been cited as contributing to the Great Moderation including central bank monetary policies, the shift from manufacturing to services in the economy, improvements in information technology and management practices, as well as reduced energy prices. The period coincided with the term of Fed chairman Alan Greenspan (1987-2006), famous for the 'Greenspan put', a policy which meant that the Fed would proactively address downturns in the stock market using its monetary policy tools. These economic factors came to prominence at the same time as the end of the Cold War (1947-1991), with the U.S. attaining a new level of hegemony in global politics, as its main geopolitical rival, the Soviet Union, no longer existed. During the Great Moderation, the U.S. experienced a recession twice, between July 1990 and March 1991, and again from March 2001 tom November 2001, however, these relatively short recessions did not knock the U.S. off its growth path. The build up of household and corporate debt over the early 2000s eventually led to the Global Financial Crisis, as the bursting of the U.S. housing bubble in 2007 reverberated across the financial system, with a subsequent credit freeze and mass defaults.

This dataset contains economic indicators for Pakistan spanning from 1986 to 2006 (21 years of data). Here's what the dataset includes: Dataset Overview:

Time Period: 1986-2006 Country: Pakistan Purpose: Inflation forecasting analysis

Variables/Columns:

Year - Time period identifier Inflation - Inflation rates (ranging from about 2.9% to 12.4%) [Column C] - Unlabeled column with values like 16.65, 17.4, 18, etc. GDP Growth - Economic growth rates (ranging from 1% to 7.8%) Unemployment - Unemployment rates (mostly between 3-8%) Broad Money - Monetary supply indicator (values in hundreds) Exports - Export values Imports - Import values Oil rents - Oil-related economic indicator (mostly below 1.0) Remittances - Foreign remittance values

Key Characteristics:

Comprehensive macroeconomic dataset Covers multiple economic indicators that typically influence inflation Suitable for econometric analysis and forecasting models Includes both monetary (broad money, remittances) and real sector variables (GDP, unemployment) Trade variables (exports/imports) for external sector analysis

This appears to be a well-structured dataset for studying inflation dynamics and building forecasting models for Pakistan's economy.

Description:

This dataset provides key economic indicators from various countries between 2010 and 2023. The dataset includes monthly data on inflation rates, GDP growth rates, unemployment rates, interest rates, and stock market index values. The data has been sourced from reputable global financial institutions and is suitable for economic analysis, machine learning models, and forecasting economic trends.

Data Sources:

The data has been generated to simulate real-world economic conditions, mimicking information from trusted sources like: - World Bank for GDP growth and inflation data - International Monetary Fund (IMF) for macroeconomic data - OECD for labor market statistics - National Stock Exchanges for stock market index values

Columns:

1. Date: The specific date (in Year/Month/Day format) representing when the data was collected.
2. Country: The country the data pertains to (e.g., USA, Germany, Japan).
3. Inflation Rate (%): The rate of inflation for that country, showing how fast prices for goods and services are increasing.
4. GDP Growth Rate (%): The percentage growth of the country’s Gross Domestic Product (GDP), indicating economic expansion or contraction.
5. Unemployment Rate (%): The percentage of the working-age population that is unemployed.
6. Interest Rate (%): The central bank's interest rate, used to control inflation and influence the economy.
7. Stock Index Value: The value of the country’s main stock market index, reflecting the performance of the stock market.

Potential Uses: - Economic Analysis: Researchers and analysts can use this dataset to study trends in inflation, GDP growth, unemployment, and other economic factors. - Machine Learning: This dataset can be used to train models for predicting economic trends or market performance. Financial Forecasting: Investors and economists can leverage this data for forecasting market movements based on economic conditions. - Comparative Studies: The dataset allows comparisons across countries and regions, offering insights into global economic performance.

At the end of 2024, Zimbabwe had the highest inflation rate in the world, at 736.11 percent change compared to the previous year. Inflation in industrialized and in emerging countries Higher inflation rates are more present in less developed economies, as they often lack a sufficient central banking system, which in turn results in the manipulation of currency to achieve short term economic goals. Thus, interest rates increase while the general economic situation remains constant. In more developed economies and in the prime emerging markets, the inflation rate does not fluctuate as sporadically. Additionally, the majority of countries that maintained the lowest inflation rate compared to previous years are primarily oil producers or small island independent states. These countries experienced deflation, which occurs when the inflation rate falls below zero; this may happen for a variety of factors, such as a shift in supply or demand of goods and services, or an outflow of capital.

Since the COVID-19 pandemic, the United States has experienced sharply rising then falling inflation alongside persistent labor market imbalances. This Economic Commentary interprets these macroeconomic dynamics, as represented by the Beveridge and Phillips curves, through the lens of a macroeconomic model. It uses the structure of the model to rationalize the debate about whether the US economy can expect a hard or soft landing. The model is surprised by the resiliency of the labor market as the US economy experienced disinflation. We suggest that the model’s limited ability to capture this resiliency is a feature of using a linear model to forecast the historically unprecedented movements seen after the pandemic among inflation, unemployment, and vacancy rates. We explain how, by adjusting the model to mimic congestion in a tight labor market and greater wage and price flexibility in a high-inflation environment, as during the post-pandemic period, the model can then capture what has been a path consistent with a soft landing.

This dataset provides a comprehensive collection of monthly U.S. macroeconomic indicators spanning January 2000 to December 2024.

It was designed specifically for machine learning-based inflation forecasting and includes key economic factors historically associated with inflation trends:

• Consumer Price Index (CPI) & Inflation Rate
• Unemployment Rate
• Federal Funds Rate
• M2 Money Supply
• Crude Oil Prices (WTI)
• Producer Price Index (PPI)

Primary Goal: Build predictive models to forecast year-over-year inflation rates

Possible Use Cases:

• Forecasting inflation using machine learning models like XGBoost, Random Forest, or LSTM.
• Studying relationships between macroeconomic factors and inflationary pressure.
• Comparing classical econometric approaches with modern AI-based forecasting techniques.

Structure: Each CSV contains a Date column and corresponding metric values, making it easy to merge and align data for analysis.

License: MIT License – free to use for research and educational purposes.

For a quick summary of the case study, please click "US Economy Powerpoint" and download the Powerpoint.

This dataset was inspired by rising prices for essential goods, the abnormally high inflation rate in March of 7.9 percent of this year, and the 30 trillion-dollar debt that we have. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. All of the datasets were obtained from third party sources websites such as https://dqydj.com/household-income-by-year/ and https://www.usinflationcalculator.com/inflation/historical-inflation-rates/ and only excluding https://fred.stlouisfed.org/series/ASPUS, which is first-party data.

This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. All of the datasets were obtained from third party sources websites such as https://dqydj.com/household-income-by-year/ and https://www.usinflationcalculator.com/inflation/historical-inflation-rates/ and only excluding https://fred.stlouisfed.org/series/ASPUS, which is first-party data.

I labeled all of the datasets to be self-explanatory based off of the title of the datasets. The US Economy Notebook has most of the code that I used as well as the four of the six phases of data analysis. The last two phases are in the US Economy Powerpoint. The "US Historical Inflation Rates" dataset could have also been labeled "The Inflation Of The US Dollar Month By Month". Lastly, the Average Sales of Houses in Jan is just a filtered version of "Average Sales of Houses in the US" dataset.

United States US: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data was reported at 1.799 % in 2017. This records an increase from the previous number of 1.276 % for 2016. United States US: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data is updated yearly, averaging 1.978 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 3.699 % in 1990 and a record low of 0.759 % in 2009. United States US: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Inflation. Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years.; ; World Bank staff estimates based on World Bank national accounts data archives, OECD National Accounts, and the IMF WEO database.; ;

Eritrea ER: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data was reported at 13.341 % in 2011. This records an increase from the previous number of 11.574 % for 2010. Eritrea ER: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data is updated yearly, averaging 11.574 % from Dec 1993 (Median) to 2011, with 19 observations. The data reached an all-time high of 29.505 % in 2009 and a record low of -1.383 % in 1993. Eritrea ER: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Eritrea – Table ER.World Bank.WDI: Inflation. Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years.; ; World Bank staff estimates based on World Bank national accounts data archives, OECD National Accounts, and the IMF WEO database.; ;

View yearly updates and historical trends for Advanced Economies Inflation Rate Outlook, Average Consumer Prices. Source: International Monetary Fund. Tra…

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:/...

Bulgaria BG: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data was reported at 7.984 % in 2023. This records a decrease from the previous number of 15.918 % for 2022. Bulgaria BG: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data is updated yearly, averaging 6.356 % from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 913.213 % in 1997 and a record low of 0.072 % in 2013. Bulgaria BG: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bulgaria – Table BG.World Bank.WDI: Inflation. Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years.;World Bank staff estimates based on World Bank national accounts data archives, OECD National Accounts, and the IMF WEO database.;;

The 'Inflation Rate MoM' in Singapore measures the percentage change in the Consumer Price Index (CPI) from one month to the next, reflecting short-term price movements in the economy.-2025-03-23

United States Breakeven Inflation: 5-Year data was reported at 2.300 % in 02 Dec 2025. This records a decrease from the previous number of 2.310 % for 01 Dec 2025. United States Breakeven Inflation: 5-Year data is updated daily, averaging 1.980 % from Jan 2003 (Median) to 02 Dec 2025, with 5733 observations. The data reached an all-time high of 3.590 % in 25 Mar 2022 and a record low of 0.140 % in 19 Mar 2020. United States Breakeven Inflation: 5-Year 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.I: Breakeven Inflation Rate. [COVID-19-IMPACT]

The inflation rate in the United States is expected to decrease to 2.1 percent by 2029. 2022 saw a year of exceptionally high inflation, reaching eight percent for the year. The data represents U.S. city averages. The base period was 1982-84. In economics, the inflation rate is a measurement of inflation, the rate of increase of a price index (in this case: consumer price index). It is the percentage rate of change in prices level over time. The rate of decrease in the purchasing power of money is approximately equal. According to the forecast, prices will increase by 2.9 percent in 2024. The annual inflation rate for previous years can be found here and the consumer price index for all urban consumers here. The monthly inflation rate for the United States can also be accessed here. Inflation in the U.S.Inflation is a term used to describe a general rise in the price of goods and services in an economy over a given period of time. Inflation in the United States is calculated using the consumer price index (CPI). The consumer price index is a measure of change in the price level of a preselected market basket of consumer goods and services purchased by households. This forecast of U.S. inflation was prepared by the International Monetary Fund. They project that inflation will stay higher than average throughout 2023, followed by a decrease to around roughly two percent annual rise in the general level of prices until 2028. Considering the annual inflation rate in the United States in 2021, a two percent inflation rate is a very moderate projection. The 2022 spike in inflation in the United States and worldwide is due to a variety of factors that have put constraints on various aspects of the economy. These factors include COVID-19 pandemic spending and supply-chain constraints, disruptions due to the war in Ukraine, and pandemic related changes in the labor force. Although the moderate inflation of prices between two and three percent is considered normal in a modern economy, countries’ central banks try to prevent severe inflation and deflation to keep the growth of prices to a minimum. Severe inflation is considered dangerous to a country’s economy because it can rapidly diminish the population’s purchasing power and thus damage the GDP .

Graph and download economic data for FOMC Summary of Economic Projections for the Personal Consumption Expenditures Inflation Rate, Range, High (PCECTPIRH) from 2025 to 2028 about projection, PCE, consumption expenditures, consumption, personal, inflation, rate, and USA.

The rate at which prices for goods and services are generally rising and, as a result, currency's purchasing power is declining is known as inflation. Central banks attempt to limit inflation—and avoid deflation—in order to keep the economy running smoothly. Each unit of currency may purchase fewer products and services as prices rise. This results in a reduction in the actual value of money, a process that impacts every level of the economy, from consumers to governments. The percentage change in the cost of a basket of goods and services over a certain time period, often a year, is measured by the inflation rate. It’s a key metric for assessing the health of an economy, showing how much more expensive everyday goods and services have become. The change in the average price level of a basket of goods and services over a year is represented by the inflation rate average consumer prices (annual per cent change). It’s calculated by taking the average of prices across all months of a given year compared to the previous year. This metric is determined by averaging monthly price data and comparing it to the average of the previous year. It provides a broader view of inflation trends across a longer time frame, smoothing out any short-term volatility. The Inflation rate, end of period consumer prices (annual per cent change) reflects the price level change from the end of one period (typically December) to the end of the next period (the following December). Instead of taking an average, this rate focuses on the price level at a specific point in time, providing a snapshot of inflation. It’s calculated by comparing the Consumer Price Index (CPI) of the final month of the year with the CPI of the last month of the previous year.