According to several empirical studies US inflation and nominal interest rates as well as the real interest rate can be described as unit root processes. These results imply that nominal interest rates and expected inflation do not move one-for-one in the long run, which is incongruent with theoretical models. In this paper we introduce a new nonlinear bivariate mixture autoregressive model that seems to fit quarterly US data (1953 : II?2004 : IV) reasonably well. It is found that the three-month Treasury bill rate and inflation share a common nonlinear component that explains a large part of their persistence. The real interest rate is devoid of this component, indicating one-for-one movement of the nominal interest rate and inflation in the long run and, hence, stationarity of the real interest rate.

Core consumer prices in the United States increased 2.80 percent in May of 2025 over the same month in the previous year. This dataset provides - United States Core Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Consumer Price Index in the United States increased 0.10 percent in May of 2025 over the previous month. This dataset provides - United States Inflation Rate MoM - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This paper derives optimal monetary policy rules in setups where certainty equivalence does not hold because either central bank preferences are not quadratic, and/or the aggregate supply relation is nonlinear. Analytical results show that these features lead to sign and size asymmetries, and nonlinearities in the policy rule. Reduced-form estimates indicate that US monetary policy can be characterized by a nonlinear policy rule after 1983, but not before 1979. This finding is consistent with the view that the Fed's inflation preferences during the Volcker-Greenspan regime differ considerably from the ones during the Burns-Miller regime. The file MONTHLY.TXT contains monthly data between 1970.1 and 2000.12 arranged in five columns; the file QUARTERLY.TXT contains quarterly data between 1960.1 and 2000.4 arranged in five columns. The headings OBS, FFRATE, INF, IPI, and UNRATE denote, respectively, the date, Federal Funds rate, CPI inflation rate, Index of Industrial Production, and Unemployment Rate. Additional details can be found the section entitled 3.1 DATA of the paper.

Graph and download economic data for Inflation, consumer prices for the United States (FPCPITOTLZGUSA) from 1960 to 2024 about consumer, CPI, inflation, price index, indexes, price, and USA.

Inflation Rate in Brazil decreased to 5.32 percent in May from 5.53 percent in April of 2025. This dataset provides - Brazil Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in Vietnam increased to 3.24 percent in May from 3.12 percent in April of 2025. This dataset provides the latest reported value for - Vietnam Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset includes various economic indicators such as stock market performance, inflation rates, GDP, interest rates, employment data, and housing index, all of which are crucial for understanding the state of the economy. By analysing this dataset, one can gain insights into the causes and effects of past recessions in the US, which can inform investment decisions and policy-making.

There are 20 columns and 343 rows spanning 1990-04 to 2022-10

The columns are:

1. Price: Price column refers to the S&P 500 lot price over the years. The S&P 500 is a stock market index that measures the performance of 500 large companies listed on stock exchanges in the United States. This variable represents the value of the S&P 500 index from 1980 to present. Industrial Production: This variable measures the output of industrial establishments in the manufacturing, mining, and utilities sectors. It reflects the overall health of the manufacturing industry, which is a key component of the US economy.

2. INDPRO: Industrial production measures the output of the manufacturing, mining, and utility sectors of the economy. It provides insights into the overall health of the economy, as a decline in industrial production can indicate a slowdown in economic activity. This data can be used by policymakers and investors to assess the state of the economy and make informed decisions.

3. CPI: CPI stands for Consumer Price Index, which measures the change in the prices of a basket of goods and services that consumers purchase. CPI inflation represents the rate at which the prices of goods and services in the economy are increasing.

4. Treasure Bill rate (3 month to 30 Years): Treasury bills (T-bills) are short-term debt securities issued by the US government. This variable represents the interest rates on T-bills with maturities ranging from 3 months to 30 years. It reflects the cost of borrowing money for the government and provides an indication of the overall level of interest rates in the economy.

5. GDP: GDP stands for Gross Domestic Product, which is the value of all goods and services produced in a country. This dataset is taking into account only the Nominal GDP values. Nominal GDP represents the total value of goods and services produced in the US economy without accounting for inflation.

6. Rate: The Federal Funds Rate is the interest rate at which depository institutions lend reserve balances to other depository institutions overnight. It is set by the Federal Reserve and is used as a tool to regulate the money supply in the economy.

7. BBK_Index: The BBKI are maintained and produced by the Indiana Business Research Center at the Kelley School of Business at Indiana University. The BBK Coincident and Leading Indexes and Monthly GDP Growth for the U.S. are constructed from a collapsed dynamic factor analysis of a panel of 490 monthly measures of real economic activity and quarterly real GDP growth. The BBK Leading Index is the leading subcomponent of the cycle measured in standard deviation units from trend real GDP growth.

8. Housing Index: This variable represents the value of the housing market in the US. It is calculated based on the prices of homes sold in the market and provides an indication of the overall health of the housing market.

9. Recession binary column: This variable is a binary indicator that takes a value of 1 when the US economy is in a recession and 0 otherwise. It is based on the official business cycle dates provided by the National Bureau of Economic Research.

Inflation Rate in India decreased to 2.82 percent in May from 3.16 percent in April of 2025. This dataset provides - India Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The dataset captures the dynamic fluctuations in the exchange rate between the United States Dollar (USD) and the Pakistani Rupee (PKR) over an extensive period, spanning from February 1992 to November 2023. This dataset is a valuable resource for analysts, economists, and researchers seeking to understand the historical trends, patterns, and factors influencing the USD to PKR exchange rate.

Dataset Overview:

Temporal Range: Start Date: February 1992 End Date: November 2023

Frequency: Daily exchange rates

Data Fields: Date: The date for each recorded exchange rate. USD to PKR Exchange Rate: The exchange rate indicating how much one USD is equivalent to in PKR on a given day. Change: Change between last price and today Price

Key Insights and Potential Analysis Areas:

Trend Analysis: Identify long-term trends in the exchange rate over the entire duration. Highlight periods of stability, volatility, or significant fluctuations.

Seasonal Patterns: Explore if there are any recurring seasonal patterns influencing the exchange rate.

Economic Events: Correlate major economic events, both global and domestic, with corresponding shifts in the exchange rate.

Impact of Policies: Assess the impact of monetary and fiscal policies on the USD to PKR exchange rate.

Currency Market Dynamics: Analyze how market dynamics, including demand and supply forces, influence the exchange rate.

Inflation and Interest Rates: Examine the relationship between inflation rates, interest rates, and the exchange rate.

Potential Applications:

Investment Planning: Assist investors in making informed decisions based on historical exchange rate trends.

Risk Management: Aid businesses in assessing and managing currency risk exposure.

Economic Research: Facilitate research on the impact of economic indicators on exchange rates.

Government Policy Evaluation: Provide insights for policymakers in evaluating the effectiveness of monetary and fiscal policies.

Conclusion:

This comprehensive USD to PKR exchange rate dataset offers a wealth of information for various stakeholders. Whether analyzing market trends, conducting economic research, or making strategic financial decisions, this dataset serves as a valuable tool for understanding the historical dynamics of the USD to PKR exchange rate over more than three decades.

Average weekly earnings for the whole economy, for total and regular pay, in real terms (adjusted for consumer price inflation), UK, monthly, seasonally adjusted.

Inflation Rate in Japan decreased to 3.50 percent in May from 3.60 percent in April of 2025. This dataset provides the latest reported value for - Japan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Inflation Rate in Russia decreased to 10.20 percent in April from 10.30 percent in March of 2025. This dataset provides - Russia Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in China remained unchanged at -0.10 percent in May. This dataset provides - China Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Center Point. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Center Point, the median income for all workers aged 15 years and older, regardless of work hours, was $50,000 for males and $43,375 for females.

Based on these incomes, we observe a gender gap percentage of approximately 13%, indicating a significant disparity between the median incomes of males and females in Center Point. Women, regardless of work hours, still earn 87 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In Center Point, among full-time, year-round workers aged 15 years and older, males earned a median income of $78,906, while females earned $57,772, leading to a 27% gender pay gap among full-time workers. This illustrates that women earn 73 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Remarkably, across all roles, including non-full-time employment, women displayed a lower gender pay gap percentage. This indicates that Center Point offers better opportunities for women in non-full-time positions.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Center Point median household income by race. You can refer the same here

Inflation Rate in Argentina decreased to 43.50 percent in May from 47.30 percent in April of 2025. This dataset provides the latest reported value for - Argentina Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Inflation Rate in Germany remained unchanged at 2.10 percent in May. This dataset provides the latest reported value for - Germany Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Reasnor. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Reasnor, the median income for all workers aged 15 years and older, regardless of work hours, was $47,500 for males and $43,750 for females.

Based on these incomes, we observe a gender gap percentage of approximately 8%, indicating a significant disparity between the median incomes of males and females in Reasnor. Women, regardless of work hours, still earn 92 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In Reasnor, among full-time, year-round workers aged 15 years and older, males earned a median income of $75,000, while females earned $56,250, leading to a 25% gender pay gap among full-time workers. This illustrates that women earn 75 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Remarkably, across all roles, including non-full-time employment, women displayed a lower gender pay gap percentage. This indicates that Reasnor offers better opportunities for women in non-full-time positions.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Reasnor median household income by race. You can refer the same here

Inflation Rate in Pakistan increased to 3.50 percent in May from 0.30 percent in April of 2025. This dataset provides the latest reported value for - Pakistan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Inflation Rate in Thailand decreased to -0.57 percent in May from -0.22 percent in April of 2025. This dataset provides the latest reported value for - Thailand Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.