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 ---

Chicago Fed National Activity Index in the United States decreased to -0.25 points in April from 0.03 points in March of 2025. This dataset provides the latest reported value for - United States Chicago Fed National Activity Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

****Dataset Overview**** This dataset contains historical macroeconomic data, featuring key economic indicators in the United States. It includes important metrics such as the Consumer Price Index (CPI), Retail Sales, Unemployment Rate, Industrial Production, Money Supply (M2), and more. The dataset spans from 1993 to the present and includes monthly data on various economic indicators, processed to show their rate of change (either percentage or absolute difference, depending on the indicator).

provenance

The data in this dataset is sourced from the Federal Reserve Economic Data (FRED) database, hosted by the Federal Reserve Bank of St. Louis. FRED provides access to a wide range of economic data, including key macroeconomic indicators for the United States. My work involved calculating the rate of change (ROC) for each indicator and reorganizing the data into a more usable format for analysis. For more information and access to the full database, visit FRED's website.

Purpose and Use for the Kaggle Community:

This dataset is a valuable resource for data scientists, economists, and analysts interested in understanding macroeconomic trends, performing time series analysis, or building predictive models. With the rate of change included, users can quickly assess the growth or contraction in these indicators month-over-month. This dataset can be used for:

• Exploratory Data Analysis (EDA): Understanding historical economic trends. -Time Series Forecasting: Building models to predict future economic conditions. -Macroeconomic Analysis: Analyzing the relationship between various economic indicators. -Machine Learning Projects: Using the data as features to predict financial or economic outcomes. -By utilizing this dataset, users can perform in-depth analysis on the impact of macroeconomic changes, compare the historical performance of various indicators, and experiment with different time series forecasting techniques.

****Column Descriptions****

Year: The year of the observation.

Month: The month of the observation (1-12).

Industrial Production: Monthly data on the total output of US factories, mines, and utilities.

Manufacturers' New Orders: Durable Goods: Measures the value of new orders placed with manufacturers for durable goods, indicating future production activity.

Consumer Price Index (CPIAUCSL): A measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.

Unemployment Rate: The percentage of the total labor force that is unemployed but actively seeking employment.

Retail Sales: The total receipts of retail stores, indicating consumer spending and economic activity.

Producer Price Index: Measures the average change over time in the selling prices received by domestic producers for their output.

Personal Consumption Expenditures (PCE): A measure of the prices paid by consumers for goods and services, used in calculating inflation.

National Home Price Index: A measure of changes in residential real estate prices across the country.

All Employees, Total Nonfarm: The number of nonfarm payroll employees, an important indicator of the labor market.

Labor Force Participation Rate: The percentage of the working-age population that is either employed or actively looking for work.

Federal Funds Effective Rate: The interest rate at which depository institutions lend reserve balances to other depository institutions overnight.

Building Permits: The number of building permits issued for residential and non-residential buildings, a leading indicator of construction activity.

Money Supply (M2): The total money supply, including cash, checking deposits, and easily convertible near money.

Personal Income: The total income received by individuals from all sources, including wages, investments, and government transfers.

Trade Balance: The difference between a country's imports and exports, indicating the net trade flow.

Consumer Sentiment: The index reflecting consumer sentiment and expectations for the future economic outlook.

Consumer Confidence: A measure of how optimistic or pessimistic consumers are regarding their expected financial situation and the economy.

Notes on Interest Rates Please note that for the Federal Funds Effective Rate (FEDFUNDS), the dataset includes the absolute change in basis points (bps), not the rate of change. This means that the dataset reflects the direct change in the interest rate rather than the percentage change month-over-month. The change is represented in basis points, where 1 basis point equals 0.01%.

We apply a discrete choice approach to model the empirical behaviour of the Federal Reserve in changing the federal funds target rate, the benchmark of short-term market interest rates in the US. Our methods allow the explanatory variables to be nonstationary as well as stationary. This feature is particularly useful in the present application as many economic fundamentals that are monitored by the Fed and are believed to affect decisions to adjust interest rate targets display some nonstationarity over time. The chosen model successfully predicts the majority of the target rate changes during the time period considered (1994-2001) and helps to explain strings of similar intervention decisions by the Fed. Based on the model-implied optimal interest rate, our findings suggest that there is a lag in the Fed's reaction to economic shocks during this period.

View data of the Effective Federal Funds Rate, or the interest rate depository institutions charge each other for overnight loans of funds.

Philadelphia Fed Manufacturing Index in the United States increased to -4 points in May from -26.40 points in April of 2025. This dataset provides the latest reported value for - United States Philadelphia Fed Manufacturing Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In the literature, the consensus about the importance of the independence of the central banks towards stable economic growth has been proven (e.g. Barro and Gordon 1983). The empirical papers studying this problem follow Cukierman, Webb, and Neyapti (1992) central bank independence index which does not include some important factors such as rule of law in the given country and defines the components of independence too generally. In this project, we aim to build an alternative index that will measure the central bank independence in more detail which will account for the rule of law and other relevant aspects that indirectly affect the true independence of the central bank. Obtaining the index, we will study whether the independence of the central bank has a positive effect on maintaining the given central bank's primary target. Furthermore, in this research, we aim to test whether our results differ from the previous findings in the empirical literature on the central bank independence and its effect on stable prices. Based on the obtained results, an optimal legislature of the central bank's independence shall be suggested. This panel dataset provides an assessment of the independence of central banks in 21 OECD countries (excluding the Eurozone), focusing on their monetary policy autonomy as determined by legislation in 2010, 2015, and 2020. Our data collection adopts a novel approach, building upon theinnovating methodology proposed by Cukierman et al. (1992), while incorporating revised components of the index that place greater emphasis on current standards of central bank independence. Additionally, we introduce new criteria to evaluate budgetary independence, an important aspect of central bank autonomy (Swinburne and Castello-Branco, 1991). The dataset serves as a valuable resource for empirical studies seeking to analyze the impact of monetary policy independence on economic performance. Furthermore, policymakers can draw insights from this index to enhance legislative frameworks and promote stronger performance in central bank independence.

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.

This data set is a subset of the "Records of foreign capital" (Registros de capitais estrangeiros", RCE) published by the Central Bank of Brazil (CBB) on their website.The data set consists of three data files and three corresponding metadata files. All files are in openly accessible .csv or .txt formats. See detailed outline below for data contained in each. Data files contain transaction-specific data such as unique identifier, currency, cancelled status and amount. Metadata files outline variables in the corresponding data file.RCE_Unclean_full_dataset.csv - all transactions published to the Central Bank website from the four main categories outlined belowMetadata_Unclean_full_dataset.csvRCE_Unclean_cancelled_dataset.csv - data extracted from the RCE_Unclean_full_dataset.csv where transactions were registered then cancelledMetadata_Unclean_cancelled_dataset.csvRCE_Clean_selection_dataset.csv - transaction data extracted from RCE_Unclean_full_dataset.csv and RCE_Unclean_cancelled_dataset.csv for the nine companies and criteria identified belowMetadata_Clean_selection_dataset.csvThe data include the period between October 2000 and July 2011. This is the only time span for the data provided by the Central Bank of Brazil at this stage. The records were published monthly by the Central Bank of Brazil as required by Art. 66 in Decree nº 55.762 of 17 February 1965, modified by Decree nº 4.842 of 17 September 2003. The records were published on the bank’s website starting October 2000, as per communique nº 011489 of 7 October 2003. This remained the case until August 2011, after which the amount of each transaction was no longer disclosed (and publication of these stopped altogether after October 2011). The disclosure of the records was suspended in order to review their legal and technical aspects, and ensure their suitability to the requirements of the rules governing the confidentiality of the information (Law nº 12.527 of 18 November 2011 and Decree nº 7724 of May 2012) (pers. comm. Central Bank of Brazil, 2016. Name of contact available upon request to Authors).The records track transfers of foreign capital made from abroad to companies domiciled in Brazil, with information on the foreign company (name and country) transferring the money, and on the company receiving the capital (name and federative unit). For the purpose of this study, we consider the four categories of foreign capital transactions which are published with their amount and currency in the Central Bank’s data, and which are all part of the “Register of financial transactions” (abbreviated RDE-ROF): loans, leasing, financed import and cash in advance (see below for a detailed description). Additional categories exist, such as foreign direct investment (RDE-IED) and External Investment in Portfolio (RDE-Portfólio), for which no amount is published and which are therefore not included.We used the data posted online as PDFs on the bank’s website, and created a script to extract the data automatically from these four categories into the RCE_Unclean_full_dataset.csv file. This data set has not been double-checked manually and may contain errors. We used a similar script to extract rows from the "cancelled transactions" sections of the PDFs into the RCE_Unclean_cancelled_dataset.csv file. This is useful to identify transactions that have been registered to the Central Bank but later cancelled. This data set has not been double-checked manually and may contain errors.From these raw data sets, we conducted the following selections and calculations in order to create the RCE_Clean_selection_dataset.csv file. This data set has been double-checked manually to secure that no errors have been made in the extraction process.We selected all transactions whose recipient company name corresponds to one of these nine companies, or to one of their known subsidiaries in Brazil, according to the list of subsidiaries recorded in the Orbis database, maintained by Bureau Van Dijk. Transactions are included if the recipient company name matches one of the following:- the current or former name of one of the nine companies in our sample (former names are identified using Orbis, Bloomberg’s company profiles or the company website);- the name of a known subsidiary of one of the nine companies, if and only if we find evidence (in Orbis, Bloomberg’s company profiles or on the company website) that this subsidiary was owned at some point during the period 2000-2011, and that it operated in a sector related to the soy or beef industry (including fertilizers and trading activities).For each transaction, we extracted the name of the company sending capital and when possible, attributed the transaction to the known ultimate owner.The name of the countries of origin sometimes comes with typos or different denominations: we harmonized them.A manual check of all the selected data unveiled that a few transactions (n=14), appear twice in the database while bearing the same unique identification number. According to the Central Bank of Brazil (pers. comm., November 2016), this is due to errors in their routine of data extraction. We therefore deleted duplicates in our database, keeping only the latest occurrence of each unique transaction. Six (6) transactions recorded with an amount of zero were also deleted. Two (2) transactions registered in August 2003 with incoherent currencies (Deutsche Mark and Dutch guilder, which were demonetised in early 2002) were also deleted.To secure that the import of data from PDF to the database did not contain any systematic errors, for instance due to mistakes in coding, data were checked in two ways. First, because the script identifies the end of the row in the PDF using the amount of the transaction, which can sometimes fail if the amount is not entered correctly, we went through the extracted raw data (2798 rows) and cleaned all rows whose end had not been correctly identified by the script. Next, we manually double-checked the 486 largest transactions representing 90% of the total amount of capital inflows, as well as 140 randomly selected additional rows representing 5% of the total rows, compared the extracted data to the original PDFs, and found no mistakes.Transfers recorded in the database have been made in different currencies, including US dollars, Euros, Japanese Yens, Brazilian Reais, and more. The conversion to US dollars of all amounts denominated in other currencies was done using the average monthly exchange rate as published by the International Monetary Fund (International Financial Statistics: Exchange rates, national currency per US dollar, period average). Due to the limited time period, we have not corrected for inflation but aggregated nominal amounts in USD over the period 2000-2011.The categories loans, cash in advance (anticipated payment for exports), financed import, and leasing/rental, are those used by the Central Bank of Brazil in their published data. They are denominated respectively: “Loans” (“emprestimos” in original source) - : includes all loans, either contracted directly with creditors or indirectly through the issuance of securities, brokered by foreign agents. “Anticipated payment for exports” (“pagamento/renovacao pagamento antecipado de exportacao” in original source): defined as a type of loan (used in trade finance)“Financed import” (“importacao financiada” in original source): comprises all import financing transactions either direct (contracted by the importer with a foreign bank or with a foreign supplier), or indirect (contracted by Brazilian banks with foreign banks on behalf of Brazilian importers). They must be declared to the Central Bank if their term of payment is superior to 360 days.“Leasing/rental” (“arrendamento mercantil, leasing e aluguel” in original source) : concerns all types of external leasing operations consented by a Brazilian entity to a foreign one. They must be declared if the term of payment is superior to 360 days.More information about the different categories can be found through the Central Bank online.(Research Data Support provided by Springer Nature)

Dallas Fed Manufacturing Index in the United States increased to -15.30 points in May from -35.80 points in April of 2025. This dataset provides the latest reported value for - United States Dallas Fed Manufacturing Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The "Brazilian Payment Methods" dataset provides comprehensive monthly statistics on payment transactions in Brazil in its various forms, sourced from the Banco Central do Brasil (Banco Central do Brasil). This dataset is invaluable for researchers, analysts and policymakers interested in understanding the dynamics of payment methods in Brazil's financial ecosystem.

It is possible to follow the evolution of different payment methods over time, such as the rise of PIX in contrast to the decline of methods such as DOC. This allows for a detailed analysis of how the adoption and use of different payment instruments has changed over the years.

Main features:

Period: The dataset covers monthly data starting from January 2016. Data sources: All data comes directly from the Central Bank of Brazil, ensuring high accuracy and reliability. Payment methods included: PIX: Instant payments. TED (Electronic Transfer Available): High value transfers. TEC (Electronic Credit Transfer): Commonly used to pay salaries DOC (Documentary Credit Order): Interbank transfers. Check: Paper payment method. Boleto: Boletos issued by the bank. Metrics: The dataset includes the quantity and total value of transactions for each payment method. Columns:

YearMonth: The reference month in YYYYMM format. quantityPix: Number of PIX transactions. valuePix: Total value of PIX transactions. quantityTED: The number of TED transactions. valueTED: The total value of TED transactions. quantityTEC: The number of TEC transactions. valueTEC: The total value of TEC transactions. quantityBankCheck: The number of check transactions. valueBankCheck: The total value of check transactions. quantityBrazilianBoletoPayment: Number of boleto transactions. valueBrazilianBoletoPayment: The total value of the boleto transactions. quantityDOC: The number of DOC transactions. valueDOC: The total value of DOC transactions.

This dataset can be used for a variety of analyses, including but not limited to:

Trend analysis: Track the growth or decline in the use of different payment methods over time. Economic Research: Study the impact of economic events on payment behavior. Financial Planning: Assistance in decision-making for financial institutions and companies. Policy making: Inform policy decisions regarding the regulation and promotion of payment methods. Data collection:

Data is collected and updated monthly, ensuring that users have access to the most current information. The script used to collect and update data was designed to be executed automatically, fetching the most recent data from the Central Bank of Brazil API.

Column Names Translation:

The original column names from the Central Bank of Brazil's API have been translated into English where possible. For instance:

    AnoMes has been translated to YearMonth
    quantidadePix has been translated to quantityPix
    valorPix has been translated to valuePix
    quantidadeTED has been translated to quantityTED
    valorTED has been translated to valueTED
    quantidadeTEC has been translated to quantityTEC
    valorTEC has been translated to valueTEC
    quantidadeCheque has been translated to quantityBankCheck
    valorCheque has been translated to valueBankCheck
    quantidadeBoleto has been translated to quantityBrazilianBoletoPayment
    valorBoleto has been translated to valueBrazilianBoletoPayment
    quantidadeDOC has been translated to quantityDOC
    valorDOC has been translated to valueDOC

Thanks:

We thank the Central Bank of Brazil for providing open access to this valuable data. For more details, visit Central Bank of Brazil – Open Data.

License:

This dataset is licensed under the Open Data Commons Open Database License (ODbL). You are free to share, modify and use the data, as long as you attribute the source.

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Federal Debt: Total Public Debt (GFDEBTN) from Q1 1966 to Q1 2025 about public, debt, federal, government, and USA.

Graph and download economic data for NBER based Recession Indicators for the United States from the Period following the Peak through the Trough (USREC) from Dec 1854 to May 2025 about peak, trough, recession indicators, and USA.

Inflation Rate in the United States decreased to 2.30 percent in April from 2.40 percent in March of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

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

The data presented in this repository can be fed into the codes provided in this GitHub repository to reproduce the results of the following paper:

Chowdhury, A.F.M.K., Wild, T., Zhang, Y. et al. Hydropower expansion in eco-sensitive river basins under global energy-economic change. Nat Sustain 7, 213–222 (2024). https://doi.org/10.1038/s41893-023-01260-z

Summary

In this study, we investigate how rapid economic growth and transition to low-carbon energy may impact hydropower development, with potential countervailing effects of increasingly cost-competitive variable renewable energy (VRE). We explore the effects of these forces on hydropower expansion in the world's 20 most eco-sensitive river basins, that have substantial untapped hydropower potential and ecological richness. Our investigation is based on the Global Change Analysis Model (GCAM), an integrated model of global energy-water-economy dynamics. The GCAM outputs and other data provided in this repository, in combination with the Jupyter Notebooks provided in this GitHub repository, can be used to conduct our key analysis, and reproduce the relevant results.

The Gross Domestic Product (GDP) in the United States contracted 0.20 percent in the first quarter of 2025 over the previous quarter. This dataset provides the latest reported value for - United States GDP Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Mexico was last recorded at 8.50 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.