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.

Policy interest rates in the U.S. and Europe are forecasted to decrease gradually between 2024 and 2027, following exceptional increases triggered by soaring inflation between 2021 and 2023. The U.S. federal funds rate stood at **** percent at the end of 2023, the European Central Bank deposit rate at **** percent, and the Swiss National Bank policy rate at **** percent. With inflationary pressures stabilizing, policy interest rates are forecast to decrease in each observed region. The U.S. federal funds rate is expected to decrease to *** percent, the ECB refi rate to **** percent, the Bank of England bank rate to **** percent, and the Swiss National Bank policy rate to **** percent by 2025. An interesting aspect to note is the impact of these interest rate changes on various economic factors such as growth, employment, and inflation. The impact of central bank policy rates The U.S. federal funds effective rate, crucial in determining the interest rate paid by depository institutions, experienced drastic changes in response to the COVID-19 pandemic. The subsequent slight changes in the effective rate reflected the efforts to stimulate the economy and manage economic factors such as inflation. Such fluctuations in the federal funds rate have had a significant impact on the overall economy. The European Central Bank's decision to cut its fixed interest rate in June 2024 for the first time since 2016 marked a significant shift in attitude towards economic conditions. The reasons behind the fluctuations in the ECB's interest rate reflect its mandate to ensure price stability and manage inflation, shedding light on the complex interplay between interest rates and economic factors. Inflation and real interest rates The relationship between inflation and interest rates is critical in understanding the actions of central banks. Central banks' efforts to manage inflation through interest rate adjustments reveal the intricate balance between economic growth and inflation. Additionally, the concept of real interest rates, adjusted for inflation, provides valuable insights into the impact of inflation on the economy.

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 10-Year Real Interest Rate (REAINTRATREARAT10Y) from Jan 1982 to Jul 2025 about 10-year, interest rate, interest, real, rate, and USA.

Graph and download economic data for Treasury Long-Term Average (Over 10 Years), Inflation-Indexed (DLTIIT) from 2000-01-03 to 2025-07-29 about TIPS, long-term, Treasury, yield, interest rate, interest, real, rate, and USA.

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.

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 uses a dynamic factor model for the quarterly changes in consumption goods’ prices to separate them into three components: idiosyncratic relative-price changes, aggregate relative-price changes, and changes in the unit of account. The model identifies a measure of “pure” inflation: the common component in goods’ inflation rates that has an equiproportional effect on all prices and is uncorrelated with relative price changes at all dates. The estimates of pure inflation and of the aggregate relative-price components allow us to re-examine three classic macro-correlations. First, we find that pure inflation accounts for 15-20% of the variability in overall inflation, so that most changes in inflation are associated with changes in goods’ relative prices. Second, we find that the Phillips correlation between inflation and measures of real activity essentially disappears once we control for goods’ relative-price changes. Third, we find that, at business-cycle frequencies, the correlation between inflation and money is close to zero, while the correlation with nominal interest rates is around 0.5, confirming previous findings on the link between monetary policy and inflation.

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.

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.

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.

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

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.

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 Apple share market data of 10 years can be used for educational purposes in a variety of ways, such as:

To learn about the stock market and how it works. By studying the historical price movements of Apple stock, you can learn about the different factors that can affect the stock market, such as economic conditions, interest rates, and company earnings. To develop investment strategies. By analyzing the Apple share market data, you can identify patterns and trends that can help you make better investment decisions. For example, you might notice that Apple stock tends to perform well in certain economic conditions or when the company releases new products. To learn about Apple's business. By tracking the company's stock price, you can get a sense of how investors are viewing Apple's financial performance and future prospects. This information can be helpful for making decisions about whether or not to invest in Apple stock. To conduct research on financial topics. The Apple share market data can be used to support research on a variety of financial topics, such as the impact of inflation on stock prices, the relationship between stock prices and interest rates, and the performance of different investment strategies. In addition to these educational purposes, the Apple share market data can also be used for other purposes, such as:

To create trading algorithms. Trading algorithms are computer programs that automatically buy and sell stocks based on certain criteria. The Apple share market data can be used to train trading algorithms to identify profitable trading opportunities. To develop risk management strategies. Risk management strategies are used to protect investors from losses. The Apple share market data can be used to identify risks associated with investing in Apple stock and to develop strategies to mitigate those risks. To make corporate decisions. The Apple share market data can be used by companies to make decisions about their business, such as how much to invest in research and development, how to allocate capital, and when to issue new shares. Overall, the Apple share market data is a valuable resource that can be used for a variety of educational and practical purposes. If you are interested in learning more about the stock market or investing, I encourage you to explore the Apple share market 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.

Exploring the Positive Correlation Between Interest Rates and Housing Market Recessions

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

We develop a theoretical framework that rationalizes two hypotheses of long-lasting low-interest rate episodes: deflationary-expectations-traps and secular stagnation in a unified setting. These hypotheses differ in the sign of the theoretical correlation between inflation and output growth that they imply. Using the data from Japan over 1998:Q1-2019:Q4, we find that the data favor the expectations-trap hypothesis. The superior model fit of the expectations trap relies on its ability to generate the observed negative correlation between inflation and output growth.

The World Bank Central Government Debt dataset contains information on the amount of debt owed by central governments in countries around the world. This dataset provides information on the amount of debt owed by central governments.The dataset contains data for more than 130 countries and covers a period of several decades. The data is collected from official government sources, such as national central banks, finance ministries, and debt management offices.

Some potential uses of this dataset include: * Analyzing the relationship between central government debt levels and economic indicators, such as GDP, inflation, and interest rates. * Studying the impact of different types of debt instruments on central government debt levels. * Comparing central government debt levels across different countries and regions. * Forecasting central government debt levels and analyzing the factors that drive changes in debt levels.

• Acquisition for the home of some good: car or motorbike, home furnishings, household appliances or computers and small household appliances.
• Monthly savings capacity at home.
• Retrospective assessment of the household's economic situation (6 months). Reason for the positive and negative assessment.
• Number of unemployed and job-seeking people in the surrounding area. Its evolution since 6 months.
• Retrospective and prospective assessment of the possibility of improving or finding employment in Spain (6 months).
• Retrospective and prospective assessment of the economic situation in Spain (6 months).
• Prospective assessment of the probability of acquiring durable goods for the household (1 year).
• Evolution of the saving capacity of the household (1 year).
• Prospective assessment of the household's economic situation (6 months). Reason for the positive and negative assessment.
• Prospective assessment of inflation in Spain (1 year).
• Evolution of interest rates and house prices in Spain (1 year). Intention to buy a home in the next year.
• Level of studies completed by obtaining a degree and in progress from the person interviewed.
• Household size. Family relationship between household members. Number of persons in the household with income.
• Ideology scale of the interviewee.
• Time of acquisition of Spanish nationality. Level of knowledge of Spanish of the interviewee. Country of origin.
• Participation and remembrance of vote in the 2016 general election.
• Housing tenure regime.

ABSTRACT This paper reports an empirical research on the dynamics of inflation of seventeen industrial sectors of the Brazilian economy between 1996 and 2011. From a theoretical discussion of the relationship between inflation and aggregate demand in traditional economic approaches (the New Consensus Model), Post-Keynesian and Distributive Conflict, we sought evidence of excess demand inflation and cost pressures in these sectors. The time series used were the Producer Price Index for Comprehensive Source (IPA-OG), the degree of Installed Capacity Utilization (both from FGV), the International Commodities Index (IFS/IMF), the interest rate and the nominal exchange rate (both from Brazilian Central Bank - BCB). The methodology was based on ADL Model (Autoregressive Distributed Lags). The results pointed to the absence of a strong and systematic relationship between inflation and aggregate demand, and to evidences of cost pressures, particularly international prices and the exchange of commodities as determinants of inflation dynamics of the sectors and period analyzed.