This dataset shows the Macro Economic Key Data at Current Prices, 2010 - 2022 Malaysia footnote: The value for year 2021 is estimate. The value for year 2022 is preliminary.

Analysis of ‘USA Key Economic Indicators’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/calven22/usa-key-macroeconomic-indicators on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Domino’s Pizza, like many other restaurant chains, is getting pinched by higher food costs. The company’s chief executive, Richard Allison, anticipates “unprecedented increases” in the company’s food costs, which could jump by 8-10%. He said that is three to four times what the pizza chain would normally expect in a year.

This leads to the paramount issue of inflation which affects every aspects of the economy, from consumer spending, business investment and employment rates to government programs, tax policies, and interest rates. The recent release of consumer inflation data showed prices rose at the fastest pace since 1982. Inflation forecasting is key in the conduct of monetary policy and can be used in many other ways such as preserving asset values. This dataset is a consolidated macroeconomic official statistics from 1981 to 2021, containing data available in month and quarterly format.

Content

The Core Consumer Price Index (ccpi) measures the changes in the price of goods and services, excluding food and energy due to their volatility. It measures price change from the perspective of the consumer. It is a often used to measure changes in purchasing trends and inflation.

Do note there are some null values in the dataset.

Acknowledgements

All data belongs to the U.S. Bureau of Economic Analysis official release, and are retrieved from FRED, Federal Reserve Bank of St. Louis.

Inspiration

What are some noticeable patterns or seasonality of the economy? What are the current trends of the economy? Which indicators has an effect on Core CPI or vice-versa based on predictive power or influence?

Quarterly data and monthly data can be merged with forward-fill or interpolation methods.

What is the forecast of Core CPI in 2022?

--- Original source retains full ownership of the source dataset ---

United States CA: saar: NVA: Compensation of Employees (COE) data was reported at 10,622.048 USD bn in Mar 2018. This records an increase from the previous number of 10,485.714 USD bn for Dec 2017. United States CA: saar: NVA: Compensation of Employees (COE) data is updated quarterly, averaging 3,628.984 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 272,783.000 USD bn in Dec 1958 and a record low of 280.305 USD bn in Mar 1959. United States CA: saar: NVA: Compensation of Employees (COE) data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB073: Integrated Macroeconomic Accounts: Total Economy: Current Account.

This table shows the indicators of the macroeconomic scoreboard. Furthermore, some additional indicators are shown. To identify in a timely manner existing and potential imbalances and possible macroeconomic risks within the countries of the European Union in an early stage, the European Commission has drawn up a scoreboard with fourteen indicators. This scoreboard is part of the Macroeconomic Imbalance Procedure (MIP). This table contains quarterly and annual figures for both these fourteen indicators and nine additional indicators for the Netherlands.

The fourteen indicators in the macroeconomic scoreboard are: - Current account balance as % of GDP, 3 year moving average - Net international investment position, % of GDP - Real effective exchange rate, % change on three years previously - Share of world exports, % change on five years previously - Nominal unit labour costs, % change on three years previously - Deflated house prices, % change on one year previously - Private sector credit flow as % of GDP - Private sector debt as % of GDP - Government debt as % of GDP - Unemployment rate, three year moving average - Total financial sector liabilities, % change on one year previously - Activity rate, % of total population aged 15-64, change in percentage points on three years previously - Long-term unemployment rate, % of active population aged 15-74, change in percentage points on three years previously - Youth unemployment rate, % of active population aged 15-24, change in percentage points on three years previously

The additional indicators are: - Real effective exchange rate, index - Share of world exports, % - Nominal unit labour costs, index - Households credit flow as % of GDP - Non-financial corporations credit flow as % of GDP - Household debt as % of GDP - Non-financial corporations debt as % of GDP - Activity rate, % of total population aged 15-64 - Youth unemployment rate, % of active population aged 15-24

Data available from: first quarter of 2006.

Status of the figures: Annual and quarterly data are provisional.

Changes as of July 8th 2024: None. This table has been discontinued. Statistics Netherlands has carried out a revision of the national accounts. The Dutch national accounts are recently revised. New statistical sources, methods and concepts are implemented in the national accounts, in order to align the picture of the Dutch economy with all underlying source data and international guidelines for the compilation of the national accounts. For further information see section 3.

When will new figures be published? Not applicable anymore.

In FY 2012/13, real GDP at market prices was projected to grow by 4.1%, as the economy continues to recover from the after effects of the recent global and regional shocks. This was an improvement compared to last FY 2011/12 growth of 3.4% due to the rebound in performance in formal manufacturing, mining and trade service sectors of the economy, as shown in table 1. However, this was below the target of 5.4% projected at the start of the FY 2012/13 a factor attributed to weak domestic demand coupled with slower implementation of key investment projects.

Indonesia Domestic Indicators: External Financial Liabilities to(GDP) Gross Domestic ProductAt Current Price data was reported at 53.207 % in Dec 2022. This records a decrease from the previous number of 53.470 % for Sep 2022. Indonesia Domestic Indicators: External Financial Liabilities to(GDP) Gross Domestic ProductAt Current Price data is updated monthly, averaging 63.796 % from Dec 2013 (Median) to Dec 2022, with 37 observations. The data reached an all-time high of 76.919 % in Jun 2016 and a record low of 53.207 % in Dec 2022. Indonesia Domestic Indicators: External Financial Liabilities to(GDP) Gross Domestic ProductAt Current Price data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Monetary – Table ID.KAI002: Financial System Statistics: Macroeconomic Indicator.

United States CA: saar: Equals: Net Saving data was reported at 610.010 USD bn in Sep 2018. This records an increase from the previous number of 576.087 USD bn for Jun 2018. United States CA: saar: Equals: Net Saving data is updated quarterly, averaging 186.286 USD bn from Dec 1951 (Median) to Sep 2018, with 268 observations. The data reached an all-time high of 828.709 USD bn in Mar 2015 and a record low of -410.045 USD bn in Sep 2009. United States CA: saar: Equals: Net Saving data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.AB073: Integrated Macroeconomic Accounts: Total Economy: Current Account.

ABSTRACT The article aimed to forecast the Brazilian economy’s growth potential in the 2016-2025 period, assuming the absence of changes in industrial policy. It is based on a formal growth model constrained by the balance of payments (BOP) developed by the authors and disaggregated into three sectors (farming, industry, and services). All its parameters were econometrically estimated, including those of the world economy relevant to the Brazilian economy’s performance. Assuming that the current macroeconomic management “tripod” was maintained in the country, the basic interest rate and exchange rate policy were calibrated to generate the maximum growth rate allowed by the external constraint compatible with the maintenance of inflation in target each year. Forecasts were also made about the performance of the three sectors’ key variables, resulting from such calibration. Forecasted potential GDP and productivity growth were low (even by recent historical standards) and decreasing over time, with slower growth in the industrial sector than in other ones. The results revealed the critical importance of the industrial sector for such performance, suggesting that an efficient industrial policy could significantly increase the Brazilian economy’s growth potential.

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.

IDX Composite: A Leading Indicator of Indonesian Economic Recovery?

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 publication is a summary of the national accounts. It contains macroeconomic data and concise information about the production process, the sector accounts and the labour market. More details about these subjects can be found in the tables mentioned under the third heading in this explanation: "links to relevant tables and articles". The subjects of the publication give a specification of the most important mocroeconomic totals. These macroeconomic transactions can also be found in

the chapter Macroeconomics in the printed publication National accounts of the Netherlands.Well-known macroeconomic data are: Gross Domestic Product (GDP), volume change of GDP (economic growth) and national income.

The subjects in the publication are structured as follows: - macroeconomic balancing - structure macroeconomic balancing - macroeconomic classifications

The data can be selected as follows: - Current prices, mln euro - Volume changes, % - Volume-indices, 2000 = 100 - Constant prices, at prices of 2000, mln euro - Deflators: % changes - Deflators: indices 2000 = 100 - Labour input, 1 000 full-time equivalent jobs - Labour productivity, 1 000 euro

In 2005 the national accounts have been revised for the reporting year 2001 in accordance with the conceptual changes in the international guidelines of the European Union (ESA 1995). This revision also incorporated new statistical insights and new sources.

This table has been discontinued. Data are available from: 1969 up and until 2009

Reason discontinuation: The national accounts have adapted the new classification of economic activities, NACE Rev. 2. Furthermore, tables have been restructured to improve their clarity.

United States CA: saar: Plus: Other Current Transfers Received data was reported at 864.129 USD bn in Mar 2018. This records an increase from the previous number of 834.249 USD bn for Dec 2017. United States CA: saar: Plus: Other Current Transfers Received data is updated quarterly, averaging 201.663 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 6,053.000 USD bn in Dec 1958 and a record low of 5.267 USD bn in Mar 1959. United States CA: saar: Plus: Other Current Transfers Received data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB073: Integrated Macroeconomic Accounts: Total Economy: Current Account.

The Economic Mirror is intended for publishing and commenting on current macroeconomic data as well as selected topics in the field of economic, social and environmental development. The publication discusses key indicators of economic developments and monitors the implementation of economic policy. The publication is published in Slovenian and English.

United States CA: saar: NNI: COE: Wages & Salaries data was reported at 8,612.443 USD bn in Mar 2018. This records an increase from the previous number of 8,492.991 USD bn for Dec 2017. United States CA: saar: NNI: COE: Wages & Salaries data is updated quarterly, averaging 2,933.119 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 248,432.000 USD bn in Dec 1958 and a record low of 254.011 USD bn in Mar 1959. United States CA: saar: NNI: COE: Wages & Salaries data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB073: Integrated Macroeconomic Accounts: Total Economy: Current Account.

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.

Is IHS (IHS) Poised for Growth?

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

The gini index in Israel was forecast to continuously decrease between 2024 and 2029 by in total 0.01 points. The gini is estimated to amount to 0.37 points in 2029. The Gini coefficient here measures the degree of income inequality on a scale from 0 (=total equality of incomes) to one (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the gini index in countries like Jordan and Lebanon.

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.

Short/Long Term Stocks: SPB 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

View LSEG's extensive Economic Data, including content that allows the analysis and monitoring of national economies with historical and real-time series.

United States CA: saar: NNI: PI: Interest data was reported at -215.653 USD bn in Mar 2018. This records a decrease from the previous number of -205.299 USD bn for Dec 2017. United States CA: saar: NNI: PI: Interest data is updated quarterly, averaging 0.000 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 0.000 USD bn in Dec 1985 and a record low of -226.062 USD bn in Sep 2008. United States CA: saar: NNI: PI: Interest data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB073: Integrated Macroeconomic Accounts: Total Economy: Current Account.

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.

OZ Stock: Soars on Positive Economic Data

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

Summary of the data and the variables.