The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Replication files for "Job-to-Job Mobility and Inflation" Authors: Renato Faccini and Leonardo Melosi Review of Economics and Statistics Date: February 2, 2023 -------------------------------------------------------------------------------------------- ORDERS OF TOPICS .Section 1. We explain the code to replicate all the figures in the paper (except Figure 6) .Section 2. We explain how Figure 6 is constructed .Section 3. We explain how the data are constructed SECTION 1 Replication_Main.m is used to reproduce all the figures of the paper except Figure 6. All the primitive variables are defined in the code and all the steps are commented in code to facilitate the replication of our results. Replication_Main.m, should be run in Matlab. The authors tested it on a DELL XPS 15 7590 laptop wih the follwoing characteristics: -------------------------------------------------------------------------------------------- Processor Intel(R) Core(TM) i9-9980HK CPU @ 2.40GHz 2.40 GHz Installed RAM 64.0 GB System type 64-bit operating system, x64-based processor -------------------------------------------------------------------------------------------- It took 2 minutes and 57 seconds for this machine to construct Figures 1, 2, 3, 4a, 4b, 5, 7a, and 7b. The following version of Matlab and Matlab toolboxes has been used for the test: -------------------------------------------------------------------------------------------- MATLAB Version: 9.7.0.1190202 (R2019b) MATLAB License Number: 363305 Operating System: Microsoft Windows 10 Enterprise Version 10.0 (Build 19045) Java Version: Java 1.8.0_202-b08 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode -------------------------------------------------------------------------------------------- MATLAB Version 9.7 (R2019b) Financial Toolbox Version 5.14 (R2019b) Optimization Toolbox Version 8.4 (R2019b) Statistics and Machine Learning Toolbox Version 11.6 (R2019b) Symbolic Math Toolbox Version 8.4 (R2019b) -------------------------------------------------------------------------------------------- The replication code uses auxiliary files and save the pictures in various subfolders: \JL_models: It contains the equations describing the model including the observation equations and routine used to solve the model. To do so, the routine in this folder calls other routines located in some fo the subfolders below. \gensystoama: It contains a set of codes that allow us to solve linear rational expectations models. We use the AMA solver. More information are provided in the file AMASOLVE.m. The codes in this subfolder have been developed by Alejandro Justiniano. \filters: it contains the Kalman filter augmented with a routine to make sure that the zero lower bound constraint for the nominal interest rate is satisfied in every period in our sample. \SteadyStateSolver: It contains a set of routines that are used to solved the steady state of the model numerically. \NLEquations: It contains some of the equations of the model that are log-linearized using the symbolic toolbox of matlab. \NberDates: It contains a set of routines that allows to add shaded area to graphs to denote NBER recessions. \Graphics: It contains useful codes enabling features to construct some of the graphs in the paper. \Data: it contains the data set used in the paper. \Params: It contains a spreadsheet with the values attributes to the model parameters. \VAR_Estimation: It contains the forecasts implied by the Bayesian VAR model of Section 2. The output of Replication_Main.m are the figures of the paper that are stored in the subfolder \Figures SECTION 2 The Excel file "Figure-6.xlsx" is used to create the charts in Figure 6. All three panels of the charts (A, B, and C) plot a measure of unexpected wage inflation against the unemployment rate, then fits separate linear regressions for the periods 1960-1985,1986-2007, and 2008-2009. Unexpected wage inflation is given by the difference between wage growth and a measure of expected wage growth. In all three panels, the unemployment rate used is the civilian unemployment rate (UNRATE), seasonally adjusted, from the BLS. The sheet "Panel A" uses quarterly manufacturing sector average hourly earnings growth data, seasonally adjusted (CES3000000008), from the Bureau of Labor Statistics (BLS) Employment Situation report as the measure of wage inflation. The unexpected wage inflation is given by the difference between earnings growth at time t and the average of earnings growth across the previous four months. Growth rates are annualized quarterly values. The sheet "Panel B" uses quarterly Nonfarm Business Sector Compensation Per Hour, seasonally adjusted (COMPNFB), from the BLS Productivity and Costs report as its measure of wage inflation. As in Panel A, expected wage inflation is given by the... Visit https://dataone.org/datasets/sha256%3A44c88fe82380bfff217866cac93f85483766eb9364f66cfa03f1ebdaa0408335 for complete metadata about this dataset.

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.

S&P 500: A Bull or a Bear?

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

Unemployment Rate in Australia remained unchanged at 4.10 percent in February. This dataset provides - Australia Unemployment Rate at 5.8% in December - 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.

Retractable Roller Coaster Ride (RVP): Ready for a Wild Ride?

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

Unemployment Rate in the United Kingdom remained unchanged at 4.40 percent in January. This dataset provides the latest reported value for - United Kingdom Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset covers ballots 457-58, 460-68 spanning January-February, April-December 1982 (March exists but is missing from the dataset). The dataset contains the data resulting from these polls in ASCII. The ballots are as follows: 457-1 - January This Gallup poll seeks the opinions of Canadians, on predominantly political issues. The questions ask opinions about political leaders and political issues within the country and abroad. There are also questions on other topics of interest and importance to the country and government, such as physically abused children, married women who work and changes in standard of living. The respondents were also asked questions so that they could be grouped according to geographic and social variables. Topics of interest include: allowing paid maternity leave; approval of Broadbent as NDP leader; approval of Clark as leader of the Conservative party; approval of Trudeau as Prime Minister; being involved with charities; the best political party to handle energy, unemployment; energy and to unify Canada; Canada-UK relations; changing the standard of living; children who are physically abused; married women who work; talking about politics with friends; and US-Canada relations. Basic demographic variables are also included. 458-1-2 - February This Gallup poll seeks the opinions of Canadians, on predominantly political issues. The questions ask opinions about political leaders and political issues within the country. There are also questions on other topics of interest and importance to the country and government, such as the changing standard of living, inflation and unemployment. The respondents were also asked questions so that they could be grouped according to geographic and social variables. Topics of interest include: the approval of Broadbent as NDP leader; the approval of Clark as leader of the Conservative party; the approval of Trudeau as Prime Minister; the biggest threat to Canada's future; confidence in the United States problem solving; the dangers of pollution; the importance of Canadian owned industries and resources; increasing the standard of living; the main causes of unemployment; opposing price controls; the political party that would be best for the economy; reducing inflation; reducing unemployment and who would make the best Prime Minister. Basic demographic variables are also included. 460-1-a - April This Gallup poll seeks the opinions of Canadians, on predominantly social issues. The questions ask opinions about the ideal number of children to have and the quality of education. There are also questions on other topics of interest and importance to the country and government, such as municipal council spending and regional differences. The respondents were also asked questions so that they could be grouped according to geographical variables. Topics of interest include: the amount of power that the USSR has; community opinion of the teaching profession; the effects of regional differences in Canada; having the government share the cost of child care; how interesting work is; ideal number of children to have; involving unions in politics; learning languages in school; municipal council spending; the quality of education today, compared to the past; successfulness of family life; and wives who work. Basic demographic variables are also included. 461-1 - May This Gallup poll seeks the opinions of Canadians, on predominantly political issues. The questions ask opinions about political leaders and political issues within the country. There are also questions on other topics of interest and importance to the country and government, such as common Sunday activities; Falkland Island and smoking. The respondents were also asked questions so that they could be grouped according to geographic and social variables. Topics of interest include: attending church; common Sunday activities; the country with legitimate claims to Falkland island; deciding to have a nuclear war, rather than living under Communist rule; Falkland island dispute; influence of religion on everyday life; opinions about Broadbent as NDP leader; opinions about Clark as leader of the Conservative party; opinions about housing; opinions about the Canadian Immigration policy; opinions about Trudeau as the Liberal leader; opinions of the Canadian Constitution; political preferences; reasons for quitting smoking; smoking cigarettes; viewing religious broadcasts; who dominates the household; and with drawling Argentina's troops from Falkland island. Basic demographic variables are also included. 462-1 - June This Gallup poll seeks the opinions of Canadians, on both political and social issues. The questions ask opinions about political leaders and political issues within the country. There are also questions on other topics of interest and importance to the country and government, such as energy shortages, inflation and swimming ability. The respondents were also asked questions so that they could be grouped according to geographical variables. Topics of interest include: the approval of Broadbent as NDP leader; the approval of Clark as leader of the Conservative party; the approval of Trudeau as Prime Minister; chances of an energy shortage; chances of finding a new job if fired; the energy crisis in Canada; the government's handling of the economy; learning how to swim; the most important problem facing Canada; preferred political leader; the amount recession in the future; reducing unemployment; rising prices and income; success of controlling inflation; swimming ability; taking a job of less pay or lower status; trying to curb inflation; and using a small boat. Basic demographic variables are also included. 463-1 - July This Gallup poll seeks the opinions of Canadians, on political and social issues. Opinions on topics such as the direction Canada is going in, rising interest rates, and voting behaviour were discussed. The respondents were also asked questions so that they could be grouped according to geographical and social variables. Topics of interest include: biggest threat to Canada; business conditions; Canadian defense; direction the country is going in; disarmament; government wage and price control; interest rates; NATO; nuclear War risk; sympathy for Arabs and Israelis; US investment in Canada; voting behaviour. Basic demographic variables are also included. 463-2 - July This Gallup poll seeks the opinions of Canadians, on political and social issues. Opinions on topics such as MacEachen's budget and the federal election were discussed. The respondents were also asked questions so that they could be grouped according to geographical and social variables. Topics of interest include: Macheachen's budget; the federal election; families financial issues. Basic demographic variables are also included. 464-1 - August This Gallup poll seeks the opinions of Canadians, on political and social issues. The questions ask opinions about economic policy and the possibility a new election, as well as other important political issues within the country. There are also questions on other topics of interest and importance to the country and government, such attending night school; the importance of religion and unemployment. The respondents were also asked questions so that they could be grouped according to geographical variables. Topics of interest include: allowing civil servants to strike; attending night school; the best political party for the economy; calling an election prior to the end of the year; the closeness of student-teacher relations; confidence in the government's handling of inflation; confidence in the government's handling of unemployment; courses taken in night school; honesty and ethic standards of professions; how important religion is; the main causes of unemployment; opinions about children having a different religion then their parents; the productivity of Canadian workers; putting limits on wage increases; the quality of education today, compared to the past; urgent problems facing Canada; and who would make the best Prime Minister. Basic demographic variables are also included. 465-1 - September This Gallup poll seeks the opinions of Canadians, on political and social issues. The questions ask opinions about economic policy and the possibility a new election, as well as other important political issues within the country. There are also questions on other topics of interest and importance to the country and government, such attending night school; the importance of religion and unemployment. The respondents were also asked questions so that they could be grouped according to geographical variables. Basic demographic variables are also included. 465-4 - September This Gallup poll seeks the opinions of Canadians, on political and social issues. The questions ask opinions about economic policy and the possibility a new election, as well as other important political issues within the country. There are also questions on other topics of interest and importance to the country and government, such attending night school; the importance of religion and unemployment. The respondents were also asked questions so that they could be grouped according to geographical variables. Basic demographic variables are also included. 466-3 - October This Gallup poll seeks the opinions of Canadians, on predictions for 1983 and the chance of war. The questions ask opinions about whether or not 1983 will be better then 1982, as well as other predictions on world peace and striking unions. There are also questions on other topics of interest and importance to the country and government, such as the chances of a world war. The respondents were also asked questions so that they could be grouped according to geographic and social variables. Topics of interest include: the chances of a world war breaking out and predictions for 1983. Basic demographic variables are also included. 467-1 - November This

Unemployment Rate in South Africa decreased to 31.90 percent in the fourth quarter of 2024 from 32.10 percent in the third quarter of 2024. This dataset provides - South Africa Unemployment 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.

How Do You Pick a Stock? (LON:ONT Stock Forecast)

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.

Probabilistic AI: The Next Generation of Artificial Intelligence

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

Inflation Rate in Pakistan decreased to 1.50 percent in February from 2.40 percent in January 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 Malaysia decreased to 1.50 percent in February from 1.70 percent in January of 2025. This dataset provides - Malaysia Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in Russia increased to 10.10 percent in February from 9.90 percent in January of 2025. This dataset provides - Russia Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in Brazil increased to 5.06 percent in February from 4.56 percent in January of 2025. This dataset provides - Brazil Inflation 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.

Unilever's (ULVR) Sustainable Future: Will Growth Outpace 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