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.

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.

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.

Data was cleaned and prepared for a data visualization comparing the Federal Funds Rate to the 10-Year Breakeven Inflation Rate. The purpose of this project was to visualize a perspective of the Federal Reserve. With the Federal Reserve raising rates to control inflation, many are debating when will the Federal Reserve pause raising rates or cut rates. The 10-Year Breakeven Inflation Rate is still well above the Federal Reserve's FAIT (Flexible Average Inflation Targeting) of 2% for that reason the Federal Reserve still has room to play with the Funds Rate.

About Dataset

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Global Energy, Food, Consumer, and Producer Price Inflation: A Comprehensive Dataset for Understanding Economic Trends

Key Concepts:

1. Energy Consumer Price Inflation data.
2. Food Consumer Price Inflation data.
3. Headline Consumer Price Inflation data.
4. Official Core Consumer Price Inflation data.
5. Producer Price Inflation data.
6. 206 Countries name, Country code and IMF code.
7. 52 Years data from 1970 to 2022.

The global economy is highly complex, and understanding economic trends and patterns is crucial for making informed decisions about investments, policies, and more. One key factor that impacts the economy is inflation, which refers to the rate at which prices increase over time. The Global Energy, Food, Consumer, and Producer Price Inflation dataset provides a comprehensive collection of inflation rates across 206 countries from 1970 to 2022, covering four critical sectors of the economy.

Finally, the Global Producer Price Inflation dataset provides a detailed look at price changes at the producer level, providing insights into supply chain dynamics and trends. This data can be used to make informed decisions about investments in various sectors of the economy and to develop effective policies to manage producer price inflation.

In conclusion, the Global Energy, Food, Consumer, and Producer Price Inflation dataset provides a comprehensive resource for understanding economic trends and patterns across 206 countries. By examining this data, analysts can gain insights into the complex factors that impact the economy and make informed decisions about investments, policies, and more.

Potential User:

1. Economists and economic researchers
2. Policy makers and government officials
3. Investors and financial analysts
4. Agricultural researchers and policymakers
5. Energy analysts and policy makers
6. Food industry professionals
7. Business leaders and decision makers
8. Academics and students in economics, finance, and related fields

Acknowledgements:

The data were collected from the official website of worldbank.org

📂 Dataset Overview - Rows (Entries): 110 - Columns (Features): 6

Columns Description 1. Date - Format: MMM-YYYY (e.g., Jul-2025) - Monthly observations 1. Inflation_YoY (Year-on-Year Inflation %) - Inflation rate in percentage (YoY basis) - Range: 0.3% – 38% - Average: 11.6% - Can be treated as the dependent variable

1. Oil_Price_USD_Barrel
2. Global crude oil price (USD per barrel)
3. Range: 15.18 – 113.77

4. Average: 62.75

5. Exchange_Rate_PKR_USD

• Pakistani Rupee per US Dollar exchange rate
• Range: 104.6 – 304.8
• Average: 185.0

1. Interest_Rate

• State Bank of Pakistan policy rate (%)
• Range: 6.8% – 21.46%
• Average: 11.8%

1. Money_Supply_M2_Billion
2. Broad Money Supply (M2) in billion PKR
3. Range: 12,486 – 41,786
4. Average: 23,124

📊 Statistical Insights

Inflation Trends: High volatility observed between 2019–2023 (peaking at 38%), while in 2025 inflation dropped to ~3–4%.

Oil Price Relation: Fluctuations in crude oil prices appear linked with inflation movements.

Exchange Rate Impact: The depreciation of PKR from ~104 to 300+ significantly impacted inflation and interest rates.

Interest Rate Policy: Mostly ranged between 7–15%, but spiked to ~21% during currency crisis.

Money Supply Growth: Broad money consistently increased, adding long-term inflationary pressure.

📈**Possible Analyses for Kaggle**

1. Trend Analysis

2. Monthly inflation, oil price, exchange rate visualization.

3. Correlation Study

4. Inflation vs Oil Prices

5. Inflation vs Exchange Rate

6. Inflation vs Interest Rate

7. Forecasting Models

8. Time-Series forecasting (ARIMA, Prophet)

9. Regression models using oil prices, exchange rate, and money supply as predictors

10. Economic Insights

• Impact of global oil shocks on Pakistan’s inflation
• Role of monetary policy in inflation control
• Currency depreciation vs domestic inflation

This research note reconsiders the question of whether central bank independence (CBI) and fixed exchange rates (FIX) function as substitutes or complements. We argue that these monetary institutions have neither served as substitutes nor performed as complements for either inflation control or exchange rate stability. In terms of their substitutability, our statistical evidence shows that while CBI has been used for inflation control, FIX has been more directed toward exchange rate stability using updated datasets with these monetary institutions measured both on a de jure and de facto basis with nearly global country/year coverage from 1970 to 2020. In terms of their complementarity, our results also demonstrate that CBI was not more effective at reducing inflation when paired with greater FIX, and FIX was not more effective at promoting exchange rate stability when paired with greater CBI. If anything, both are less effective when paired with the other monetary institution.

Nigeria’s inflation has been higher than the average for African and Sub-Saharan countries for years now, and even exceeded 16 percent in 2017 – and a real, significant decrease is nowhere in sight. The bigger problem is its unsteadiness, however: An inflation rate that is bouncing all over the place, like this one, is usually a sign of a struggling economy, causing prices to fluctuate, and unemployment and poverty to increase. Nigeria’s economy - a so-called “mixed economy”, which means the market economy is at least in part regulated by the state – is not entirely in bad shape, though. More than half of its GDP is generated by the services sector, namely telecommunications and finances, and the country derives a significant share of its state revenues from oil. Because it got highTo simplify: When the inflation rate rises, so do prices, and consequently banks raise their interest rates as well to cope and maintain their profit margin. Higher interest rates often cause unemployment to rise. In certain scenarios, rising prices can also mean more panicky spending and consumption among end users, causing debt and poverty. The extreme version of this is called hyperinflation: A rapid increase of prices that is out of control and leads to bankruptcies en masse, devaluation of money and subsequently a currency reform, among other things. But does that mean that low inflation is better? Maybe, but only to a certain degree; the ECB, for example, aspires to maintain an inflation rate of about two percent so as to keep the economy stable. As soon as we reach deflation territory, however, things are starting to look grim again. The best course is a stable inflation rate, to avoid uncertainty and rash actions. Nigeria todayNigeria is one of the countries with the largest populations worldwide and also the largest economy in Africa, with its economy growing rapidly after a slump in the aforementioned year 2017. It is slated to be one of the countries with the highest economic growth over the next few decades. Demographic key indicators, like infant mortality rate, fertility rate, and the median age of the population, all point towards a bright future. Additionally, the country seems to make big leaps forward in manufacturing and technological developments, and boasts huge natural resources, including natural gas. All in all, Nigeria and its inflation seem to be on the upswing – or on the path to stabilization, as it were.

Inflation Rate in Kenya decreased to 4.50 percent in November from 4.60 percent in October of 2025. This dataset provides the latest reported value for - Kenya Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Abstract This paper aims to discuss the connection between monetary policy and public debt in Brazil, highlighting the consequences. To do so, it begins with a historical resumption of the emergence of the market for public debt and the institutions responsible for its management. This is followed by an analysis of the data on the variables related to monetary policy and public debt between 1999 and 2016. From this analysis, we observed the existence of a problematic connection between two policies - monetary and fiscal - given by the Selic rate, which is both an instrument to control the inflation and the rate that remunerates a significant portion of public debt. The paper concludes that this link reduces the effectiveness of these policies, requiring actions such as the untying of monetary policy and public debt, the increase in the term and duration of debt, the change in composition and, particularly, a reduction of the Selic rate.

This article integrates the ideas from two major lines of research on cost of equity and asset pricing: multi-factor models and ex ante accounting models. The earnings/price ratio is used as a proxy for the ex ante cost of equity, in order to explain realized returns of Brazilian companies within the period from 1995 to 2013. The initial finding was that stocks with high (low) earnings/price ratios have higher (lower) risk-adjusted realized returns, already controlled by the capital asset pricing model's beta. The results show that selecting stocks based on high earnings/price ratios has led to significantly higher risk-adjusted returns in the Brazilian market, with average abnormal returns close to 1.3% per month. We design asset pricing models including an earnings/price risk factor, i.e. high earnings minus low earnings, based on the Fama and French three-factor model. We conclude that such a risk factor is significant to explain returns on portfolios, even when controlled by size and market/book ratios. Models including the high earnings minus low earnings risk factor were better to explain stock returns in Brazil when compared to the capital asset pricing model and to the Fama and French three-factor model, having the lowest number of significant intercepts. These findings may be due to the impact of historically high inflation rates, which reduce the information content of book values, thus making the models based on earnings/price ratios better than those based on market/book ratios. Such results are different from those obtained in more developed markets and the superiority of the earnings/price ratio for asset pricing may also exist in other emerging markets.

Cost of food in the United States increased 3.10 percent in September of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The All India Consumer Price Index (CPI) is a measure of the average price changes of a basket of goods and services consumed by households in India. It is used to track inflation and assess changes in the cost of living over time.

The All India CPI is an aggregate index that combines the individual group indices to provide an overall measure of inflation for the entire country. It is typically reported on a monthly basis, reflecting the changes in prices compared to a designated base year. The base year is periodically revised to ensure the index remains relevant and reflective of current consumption patterns.

By tracking the All India CPI, policymakers, economists, and the general public can monitor the rate of inflation and make informed decisions related to economic policies, wage adjustments, investment strategies, and budgeting.

Data about World Development Indicators measured from 1960 to 2022, extracted from the World Bank database. It includes macro-economical, social, political and environmental data from all the countries and regions the world bank has data about.

It contains information about 268 countries and regions, including 48 features, all numerical. Several entries are missing for different reasons, so you may want to extract only the columns you are interested in.

The columns included in this dataset are:

• country: The country or geographic region.
• date: Date of the measurement. This column along with country can be used as index.
• agricultural_land%: Agricultural land as a % of land area of the country/region.
• forest_land%: Forest area as the % of land area of the country/region.
• land_area: Land area, measured in km^2.
• avg_precipitation: Average precipitation in depth, measured in mm per year.
• trade_in_services%: Trade in services as a % of GDP.
• control_of_corruption_estimate: Index that makes an estimate of the control of corruption.
• control_of_corruption_std: Standard error of the estimate of control of corruption.
• access_to_electricity%: Percentage of the population that has access to electricity.
• renewvable_energy_consumption%: Renewable energy consumption as a % of total final energy consumption.
• electric_power_consumption: Electric power consumption, measured in kWh per capita.
• CO2_emisions: CO2 emisions measured in kt.
• other_greenhouse_emisions: Total greenhouse gas emissions, measured in kt of CO2 equivalent.
• population_density: Population density, measured in people per km^2 of land area.
• inflation_annual%: Inflation, consumer prices, as annual %.
• real_interest_rate: Real interest rate (%).
• risk_premium_on_lending: Risk premium on lending (lending rate minus treasury bill rate, %).
• research_and_development_expenditure%: Research and development expenditure, as a percentage of GDP.
• central_goverment_debt%: Central government debt, total , as a % of GDP.
• tax_revenue%: Tax revenue as a % of GDP.
• expense%: Expense as a % of GDP.
• goverment_effectiveness_estimate: Index that makes an estimate of the Government Effectiveness.
• goverment_effectiveness_std: Standard error of the estimate of Government Effectiveness.
• human_capital_index: Human Capital Index (HCI) (scale 0-1).
• doing_business: Ease of doing business score (0 = lowest performance to 100 = best performance).
• time_to_get_operation_license: Days required to obtain an operating license.
• statistical_performance_indicators: Statistical performance indicators (SPI): Overall score (scale 0-100).
• individuals_using_internet%: Percentage of population using the internet.
• logistic_performance_index: Logistics performance index: Overall (1=low to 5=high).
• military_expenditure%: Military expenditure as a % of GDP.
• GDP_current_US: GDP (current US$).
• political_stability_estimate: Index that makes an estimate of the Political Stability and Absence of Violence/Terrorism.
• political_stability_std: Standard error of the estimate of Political Stability and Absence of Violence/Terrorism.
• rule_of_law_estimate: Index that makes an estimate of the Rule of Law.
• rule_of_law_std: Standard error of the estimate of Rule of Law.
• regulatory_quality_estimate: Index that makes an estimate of Regulatory Quality.
• regulatory_quality_std: Standard error of the estimate of Regulatory Quality.
• government_expenditure_on_education%: Government expenditure on education, total, as a % of GDP.
• government_health_expenditure%: Domestic general government health expenditure as a % of GDP.
• multidimensional_poverty_headcount_ratio%: Multidimensional poverty headcount ratio (% of total population).
• gini_index: Gini index.
• birth_rate: Birth rate, crude (per 1,000 people).
• death_rate: Death rate, crude (per 1,000 people).
• life_expectancy_at_birth: Life expectancy at birth, total (years).
• population: Total population.
• rural_population: Rural population.
• voice_and_accountability_estimate: Index that makes an estimate of Voice and Accountability.
• voice_and_accountability_std: Standard error of the estimate of Voice and Accountability.
• intentional_homicides: Intentional homicides (per 100,000 people).

Starting from 1970, Uruguay faced economic challenges that led to inflation and currency instability. To address these issues, the country implemented various measures, including adopting exchange rate controls and introducing a new currency, the Uruguayan peso. However, these efforts proved insufficient, and in the late 1980s, hyperinflation hit Uruguay. As a result, in 1993, the government introduced a new economic plan known as the "Uruguayo Plan." This plan aimed to stabilize the economy and regain control over monetary policy. Despite the reintroduction of the Uruguayan peso, the US dollar continues to hold influence in Uruguay's economy, particularly in international trade and tourism.

The source of this data is the National Institue of Statistics.

This is the link: https://www.gub.uy/instituto-nacional-estadistica/datos-y-estadisticas/estadisticas/cotizacion-monedas

The name of the file is: Cotizaciones al publico de las principales monedas

Rent Inflation in Canada increased to 5.20 percent in October from 4.80 percent in September of 2025. This dataset includes a chart with historical data for Canada Rent Inflation.

Consumer Price Index CPI in China increased to 103.20 points in April from 103.10 points in March of 2025. This dataset provides - China Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Housing Index in the United Kingdom increased to 517.10 points in October from 514.20 points in September of 2025. This dataset provides - United Kingdom House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.