The inflation rate in the United States declined significantly between June 2022 and May 2025, despite rising inflationary pressures towards the end of 2024. The peak inflation rate was recorded in June 2022, at *** percent. In August 2023, the Federal Reserve's interest rate hit its highest level during the observed period, at **** percent, and remained unchanged until September 2024, when the Federal Reserve implemented its first rate cut since September 2021. By January 2025, the rate dropped to **** percent, signalling a shift in monetary policy. What is the Federal Reserve interest rate? The Federal Reserve interest rate, or the federal funds rate, is the rate at which banks and credit unions lend to and borrow from each other. It is one of the Federal Reserve's key tools for maintaining strong employment rates, stable prices, and reasonable interest rates. The rate is determined by the Federal Reserve and adjusted eight times a year, though it can be changed through emergency meetings during times of crisis. The Fed doesn't directly control the interest rate but sets a target rate. It then uses open market operations to influence rates toward this target. Ways of measuring inflation Inflation is typically measured using several methods, with the most common being the Consumer Price Index (CPI). The CPI tracks the price of a fixed basket of goods and services over time, providing a measure of the price changes consumers face. At the end of 2023, the CPI in the United States was ****** percent, up from ****** a year earlier. A more business-focused measure is the producer price index (PPI), which represents the costs of firms.

The enduring discourse regarding the effectiveness of interest rate policy in mitigating inflation within developing economies is characterized by the interplay of structural and supply-side determinants. Moreover, extant academic literature fails to resolve the direction of causality between inflation and interest rates. Nevertheless, the prevalent adoption of interest rate-based monetary policies in numerous developing economies raises a fundamental inquiry: What motivates central banks in these nations to consistently espouse this strategy? To address this inquiry, our study leverages wavelet transformation to dissect interest rate and inflation data across a spectrum of frequency scales. This innovative methodology paves the way for a meticulous exploration of the intricate causal interplay between these pivotal macroeconomic variables for twenty-two developing economies using monthly data from 1992 to 2022. Traditional literature on causality tends to focus on short- and long-run timescales, yet our study posits that numerous uncharted time and frequency scales exist between these extremes. These intermediate scales may wield substantial influence over the causal relationship and its direction. Our research thus extends the boundaries of existing causality literature and presents fresh insights into the complexities of monetary policy in developing economies. Traditional wisdom suggests that central banks should raise interest rates to combat inflation. However, our study uncovers a contrasting reality in developing economies. It demonstrates a positive causal link between the policy rate and inflation, where an increase in the central bank’s interest rates leads to an upsurge in price levels. Paradoxically, in response to escalating prices, the central bank continues to heighten the policy rate, thereby perpetuating this cyclical pattern. Given this observed positive causal relationship in developing economies, central banks must explore structural and supply-side factors to break this cycle and regain control over inflation.

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.

What is the relationship between unemployment and inflation?

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

Graph and download economic data for Treasury Long-Term Average (Over 10 Years), Inflation-Indexed (DLTIIT) from 2000-01-03 to 2025-07-10 about TIPS, long-term, Treasury, yield, interest rate, interest, real, rate, and USA.

Graph and download economic data for 10-Year Real Interest Rate (REAINTRATREARAT10Y) from Jan 1982 to Jun 2025 about 10-year, interest rate, interest, real, rate, and USA.

The yield curve, also called the term structure of interest rates, refers to the relationship between the remaining time-to-maturity of debt securities and the yield on those securities. Yield curves have many practical uses, including pricing of various fixed-income securities, and are closely watched by market participants and policymakers alike for potential clues about the markets perception of the path of the policy rate and the macroeconomic outlook. This page provides daily estimated real yield curve parameters, smoothed yields on hypothetical TIPS, and implied inflation compensation, from 1999 to the present. Because this is a staff research product and not an official statistical release, it is subject to delay, revision, or methodological changes without advance notice.

Real interest rates describe the growth in the real value of the interest on a loan or deposit, adjusted for inflation. Nominal interest rates on the other hand show us the raw interest rate, which is unadjusted for inflation. If the inflation rate in a certain country were zero percent, the real and nominal interest rates would be the same number. As inflation reduces the real value of a loan, however, a positive inflation rate will mean that the nominal interest rate is more likely to be greater than the real interest rate. We can see this in the recent inflationary episode which has taken place in the wake of the Coronavirus pandemic, with nominal interest rates rising over the course of 2022, but still lagging far behind the rate of inflation, meaning these rate rises register as smaller increases in the real interest rate.

Economic welfare is essential in the modern economy since it directly reflects the standard of living, distribution of resources, and general social satisfaction, which influences individual and social well-being. This study aims to explore the relationship between national income accounting different attributes and the economic welfare in Pakistan. However, this study used data from 1950 to 2022, and data was downloaded from the World Bank data portal. Regression analysis is used to investigate the relationship between them and is very effective in measuring the relationship between endogenous and exogenous variables. Moreover, generalized methods of movement (GMM) are used as the robustness of the regression. Our results show that foreign direct investment outflow, Gross domestic product growth rate, GDP per capita, higher Interest, market capitalization, and population growth have a significant negative on the unemployment rate, indicating the rise in these factors leads to a decrease in the employment rate in Pakistan. Trade and savings have a significant positive impact on the unemployment rate, indicating the rise in these factors leads to an increase in the unemployment rate for various reasons. Moreover, all the factors of national income accounting have a significant positive relationship with life expectancy, indicating that an increase in these factors leads to an increase in economic welfare and life expectancy due to better health facilities, many resources, and correct economic policies. However, foreign direct investment, inflation rate, lending interest rate, and population growth have significant positive effects on age dependency, indicating these factors increase the age dependency. Moreover, GDP growth and GDP per capita negatively impact age dependency. Similarly, all the national income accounting factors have a significant negative relationship with legal rights that leads to decreased legal rights. Moreover, due to better health facilities and health planning, there is a negative significant relationship between national income accounting attributes and motility rate among children. Our study advocated the implications for the policymakers and the government to make policies for the welfare and increase the social factors.

The benchmark interest rate in Russia was last recorded at 20 percent. This dataset provides the latest reported value for - Russia Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

ARDL results for the relation between national interest rates and inflation and output gap.

The benchmark interest rate in Norway was last recorded at 4.25 percent. This dataset provides the latest reported value for - Norway Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for 5-Year Breakeven Inflation Rate (T5YIE) from 2003-01-02 to 2025-07-11 about spread, interest rate, interest, 5-year, inflation, rate, and USA.

Between January 2018 and May 2025, the United Kingdom's consumer price inflation rate showed notable volatility. The rate hit its lowest point at *** percent in August 2020 and peaked at *** percent in October 2022. By September 2024, inflation had moderated to *** percent, but the following months saw inflation increase again. The Bank of England's interest rate policy closely tracked these inflationary trends. Rates remained low at -* percent until April 2020, when they were reduced to *** percent in response to economic challenges. A series of rate increases followed, reaching a peak of **** percent from August 2023 to July 2024. The central bank then initiated rate cuts in August and November 2024, lowering the rate to **** percent, signaling a potential shift in monetary policy. In February 2025, the Bank of England implemented another rate cut, setting the bank rate at *** percent, which was further reduced to **** percent in May 2025. Global context of inflation and interest rates The UK's experience reflects a broader international trend of rising inflation and subsequent central bank responses. From January 2022 to July 2024, advanced and emerging economies alike increased their policy rates to counter inflationary pressures. However, a shift began in late 2024, with many countries, including the UK, starting to lower rates. This change suggests a potential new phase in the global economic cycle and monetary policy approach. Comparison with other major economies The UK's monetary policy decisions align closely with those of other major economies. The United States, for instance, saw its federal funds rate peak at **** percent in August 2023, mirroring the UK's rate trajectory. Similarly, central bank rates in the EU all increased drastically between 2022 and 2024. These synchronized movements reflect the global nature of inflationary pressures and the coordinated efforts of central banks to maintain economic stability. As with the UK, both the U.S. and EU began considering rate cuts in late 2024, signaling a potential shift in the global economic landscape.

This dataset supports the research exploring the impact of monetary policy instruments on the Colombian economy, focusing on the classical dichotomy and monetary neutrality. The analysis delves into how monetary policy, including instruments such as interest rates and money supply, influences both nominal and real variables in the economy. It also highlights the relationship between monetary policy and economic stability, particularly how central banks manage inflation and economic growth. Key sections explore the separation between nominal and real variables as explained by the classical dichotomy, and the principle of monetary neutrality, which argues that changes in money supply affect nominal variables without impacting real economic factors.

The dataset is structured around a combination of theoretical insights and simulations that analyze the effectiveness of monetary neutrality in the Colombian context, given both domestic and international economic challenges such as the war in Ukraine and agricultural sector disruptions. Through simulations, the dataset demonstrates the effects of monetary expansion on variables like inflation, production, and employment, providing a framework for understanding current economic trends and proposing solutions to socio-economic challenges in Colombia.

This paper investigates the long-run and short-run relationship between money supply and inflation in Pakistan, utilizing annual data spanning from 1981 to 2021. The key objective is to assess the impact of monetary policy, specifically money supply, on inflation dynamics in the country. To achieve this, the Autoregressive Distributed Lag (ARDL) bounds testing approach is employed, which is suitable for analyzing cointegration among variables with mixed integration orders. The results reveal both short and long-run cointegration between inflation, money supply, unemployment, and interest rates. Notably, unemployment demonstrates a negative correlation with inflation, while money supply and interest rates exhibit a positive relationship. These findings underscore the importance of dedicated policy measures to manage inflation effectively. The paper concludes by recommending the establishment of a policy implementation body and collaboration between the government and the central bank to ensure financial stability and control inflation through well-calibrated monetary and fiscal policies.

The benchmark interest rate in Brazil was last recorded at 15 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Does Apple Dance to the Fed's Tune? Investigating the Relationship Between Apple's GDP and Interest Rates

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

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.

Inflation: Friend or Foe to the Stock Market?

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

This table contains 39 series, with data for starting from 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Financial market statistics (39 items: Government of Canada Treasury Bills, 1-month (composite rates); Government of Canada Treasury Bills, 2-month (composite rates); Government of Canada Treasury Bills, 3-month (composite rates);Government of Canada Treasury Bills, 6-month (composite rates); ...).

The statistic shows the inflation rate in India from 1987 to 2024, with projections up until 2030. The inflation rate is calculated using the price increase of a defined product basket. This product basket contains products and services, on which the average consumer spends money throughout the year. They include expenses for groceries, clothes, rent, power, telecommunications, recreational activities and raw materials (e.g. gas, oil), as well as federal fees and taxes. In 2024, the inflation rate in India was around 4.67 percent compared to the previous year. See figures on India's economic growth for additional information. India's inflation rate and economy Inflation is generally defined as the increase of prices of goods and services over a certain period of time, as opposed to deflation, which describes a decrease of these prices. Inflation is a significant economic indicator for a country. The inflation rate is the rate at which the general rise in the level of prices, goods and services in an economy occurs and how it affects the cost of living of those living in a particular country. It influences the interest rates paid on savings and mortgage rates but also has a bearing on levels of state pensions and benefits received. A 4 percent increase in the rate of inflation in 2011 for example would mean an individual would need to spend 4 percent more on the goods he was purchasing than he would have done in 2010. India’s inflation rate has been on the rise over the last decade. However, it has been decreasing slightly since 2010. India’s economy, however, has been doing quite well, with its GDP increasing steadily for years, and its national debt decreasing. The budget balance in relation to GDP is not looking too good, with the state deficit amounting to more than 9 percent of GDP.