此页面提供了一个表格，显示了多种商品按类别的相关性，包括能源、金属、农业、工业和牲畜。. Commodities Prices - Spot - Futures - Correlation data was updated on February of 2025. This page has actual data, historical chart, calendar and forecasts for Commodities Prices - Spot - Futures - Correlation.

The datasets for the Role of Financial Investors on Commodity Futures Risk Premium are weekly datasets for the period from 1995 to 2015 for three commodities in the energy market: crude oil (WTI), heating oil, and natural gas. These datasets contain futures prices for different maturities, open interest positions for each commodity (long and short open interest positions), and S&P 500 composite index. The selected commodities are traded on the New York Mercantile Exchange (NYMEX). The data comes from the Thomson Reuters Datastream and from the Commodity Futures Trading Commission (CFTC).

1. Separating the imported goods from the domestically produced goods on the producer price trade table, we get the imported goods trade table, which does not include the net import tax amount.2. Purpose of collection: to demonstrate the industry structure and the interdependence relationship between industry sectors.3. Data collection method: mainly based on the industrial and commercial census conducted every five years and various related statistical data.

This paper is concerned with the estimation of a model in which a possibly serially correlated stochastic process, the harvest of an agricultural commodity, generates a competitive price in a market comprising both final consumers and risk-neutral speculators who can store the commodity at a cost in the anticipation of profit. Because storage cannot be negative, the relationship between prices and harvests is inherently nonlinear and is an unpromising candidate for a linear-quadratic model, or for linearization more generally. Instead, we calculate numerically a policy function in which price is a function of two unobservable state variables, the harvest and current availability, and we use the result to fit the price data.

Many organizations quantify greenhouse emissions in their value chain. Emissions from purchased goods and services and capital goods, referred to as Scope 3 emissions in the Greenhouse Gas Protocol Scope 3 Accounting and Reporting Standard, represent a significant emissions source for many organizations. To assist in quantifying these emissions, we have developed a comprehensive set of supply chain emission factors covering all categories of goods and services in the US economy. These factors are intended for quantifying emissions from purchased goods and services using the spend-based method defined in the Greenhouse Gas Protocol Technical Guidance for Calculating Scope 3 Emissions. The factors were prepared using USEEIO models, which are a life cycle models of goods and services in the US economy. The supply chain emission factors are presented in units of kilogram emissions per US dollar of purchases for a category of goods and services with a defined life cycle scope. Sets of factors covering all sectors of the economy are provided for years from 2010 to 2016 with two levels of sector aggregation. The factors are provided for both industries and commodities, where commodities are equivalent to a category of good or service, and industries are producers of one or more commodities. A set of five data quality scores covering data reliability, temporal, geographical and technological correlation and completeness of data collection is provided along with each factor. The factors presented are as follows: 1. Supply Chain Emission Factors without Margins: emissions associated with cradle to factory gate 2. Margins of Supply Chain Emission Factors: emissions associated with factory gate to shelf, which includes emissions from transportation, wholesale and retail as well as adjustments for price markups 3. Supply Chain Emission Factors with Margins: emissions associated with cradle to shelf (equal to the sum of the above two factors) End users of products will likely find the Supply Chain Emission Factors with Margins most appropriate for their use. Organizations purchasing intermediate products at the factory gate will likely find the Supply Chain Emission Factors without Margins to be most appropriate. See the Executive Summary of the associated report for an example calculation using the factors. All factors are associated with limitations and variations in underlying data quality. We encourage the reader to carefully read the report to understand the differences across these sets, underlying assumptions in their calculation, their limitations to decide if they are appropriate for their intended use. If the reader deems the factors are appropriate, this report along with the factor data quality scores will aid in selection of factors best fit for their intended use. This dataset is associated with the following publication: Ingwersen, W., and M. Li. Supply Chain Greenhouse Gas Emission Factors for US Industries and Commodities. U.S. Environmental Protection Agency, Washington, DC, USA, 2020.

In recent years, the international community has witnessed many crisis events, and the Russia-Ukraine war, which broke out on 24th February 2022, has increased international policy uncertainty and impacted the current world commodity and financial markets. Thus, we try to capture how the Russia-Ukraine war has affected the correlation structure of international commodity and stock markets. We study six groups of commodity daily returns and one group of stock daily returns and select the sample from 24th February 2022 to 1st June 2022 as the sample during the Russia-Ukraine war; in addition, we select the sample from 1st December 2019 to 31st December 2020 as the sample during COVID-19 control group, and the sample from 1st January 2014 to 31st December 2017 as the non-extreme event control group, to explore the correlation structure of international commodity and stock markets before the war, and to compare and uncover the impact of the uncertain event of the Russia-Ukraine war on the commodity and stock markets. In this paper, the marginal density function of each series is constructed using the ARMA-GARCH-std method, and the R-Vine copula model is built based on the marginal density function to analyze the correlation relationship between each market. From the Tree1 of the Vine copula, it is found that crude oil becomes the core connecting each commodity market and the stock market during the Russia-Ukraine war. The price fluctuations of crude oil may be contagious to agricultural and precious metal markets in the same direction, while the stock market price fluctuations are inversely correlated with commodity markets. Comparison with the selected control group sample reveals that the Russia-Ukraine war increases the correlation between the markets and enhances the possibility of risk transmission. The core of the correlation structure shifts from agricultural commodities and precious metals to crude oil after the Russia-Ukraine war.

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Abstract of associated article: We investigate price and volatility risk originating in linkages between energy and agricultural commodity prices in Germany and study their dynamics over time. We propose an econometric approach to quantify the volatility and correlation risk structure, which has a large impact for investment and hedging strategies of market participants as well as for policy makers. Volatilities and their short and long run linkages are analyzed using an asymmetric dynamic conditional correlation GARCH model as well as a multivariate multiplicative volatility model. Our approach provides a flexible and accurate fitting procedure for volatility and correlation risk. We find that in the long run prices move together and preserve an equilibrium, while correlations are mostly positive with persistent market shocks. Our results reveal that concerns about biodiesel being the cause of high and volatile agricultural commodity prices are rather unjustified.

1. Express the relationship between the various input values of each department in the domestic product trading table by dividing them by the total output of that department, in order to measure the direct interrelationship between industries.2. Purpose of collection: To demonstrate the industrial structure and the interdependent relationship between industry sectors.3. Data collection methods: Mainly refer to the industrial and service censuses conducted every five years and various relevant statistical data.

1.The industry relevance table shows the number of units, both directly and indirectly purchased from various departments, required to increase the final demand for a product in a certain department by 1 unit, including domestic and imported products.2.Purpose of collection: demonstrating the industrial structure and interdependence between industry sectors.3.Method of data collection: mainly referring to the industrial and service industry censuses conducted every five years and various related statistical data.

The Platts Market Data - Americas Gas and Power dataset provides access to the full breadth and depth of our market data, including benchmarks and contract price assessments.

1. The competitive relationship degree table indicates the direct and indirect purchasing units required from each department when increasing the final demand for a product by 1 unit; this model assumes that the ratio of imports from each department to the total domestic requirements for that department is fixed, and its effect is similar to the domestic dependence table.2. Purpose of collection: To demonstrate the industry structure and mutual dependence relationship between industry sectors.3. Data collection method: Mainly based on industrial and commercial censuses conducted every five years and various related statistical data.

Platts 市場データ - 米州ガスおよび電力 データセットでは、ベンチマークや契約価格評価に関する当社の広域かつ奥深い市場データへのアクセスを提供します。

Envestnet®| Yodlee®'s Bank Statement Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

The 2015 Water-Focused Social Accounting Matrix (WF-SAM) for Jordan represents a significant enhancement over the existing SAM introduced by (Raouf et al., 2021). This enhancement lies in its comprehensive integration of the water sector at the micro-account level. Constructed with thorough attention to detail, the 2015 WF-SAM for Jordan draws upon a triad of distinct data sources. Firstly, it incorporates micro-account data derived from the foundational SAM developed by Raouf et al. in 2021, acknowledged as the most contemporary SAM for Jordan, benefiting from current national and international data sources. Secondly, a contribution stems from the 2017 reports of the General Budget Department (GBD), furnishing actual financial allocations of Jordanian water utilities (General Budget Department, 2017a, 2017f, 2017e, 2017d, 2017c, 2017b). Adapting these allocations into novel account totals and deficits within the WF-SAM framework amplifies its comprehensiveness. The third data source comes from (MWI, 2015)and (Salamah, 2021), disclosing the nuanced cash flow interplay between the water-centric accounts and the entire economy, for instance, the municipal water sector allocates 12% of its total expenditures to the electricity commodity, establishing a symbiotic relationship. This calibrated interdependence of sectors elevates the precision and disaggregation of the 2015 WF-SAM for Jordan.

Out of the convention in this documentation, the 2015 JO SAM by (Raouf et al., 2021) is used in constructing proto-SAM and is identified as "Base-SAM." To enhance and modernize the representation of the water sector within the Base-SAM, budgetary components sourced from audited national public budget documents were carefully correlated with corresponding accounts within the SAM structure, adhering to the established ISIC standard economic classifications. This alignment resulted in the creation of a preliminary SAM, although it entailed further refinement to achieve equilibrium. Employing the cross-entropy SAM estimation algorithm, as set forth by (Robinson et al., 1998)), facilitated the fulfilment of the ultimate balanced state. The resultant Water-Focused Social Accounting Matrix (WF-SAM) is enclosed within the attached Excel workbook.

The WF-SAM for Jordan, includes 155 accounts, 57 activities, 63 commodities, one transaction cost account, 13-factor accounts, ten household accounts plus one enterprise account, and seven government-related accounts: one for the government itself and six for direct and indirect taxes and subsidies, with one saving and investment account and one currency exchange account, the rest of the world is represented in one account,” RoW.” The water sector is represented in three commodity accounts, two activity accounts, and one water subsidies account.

This dataset (WF-SAM) enables future research on an array of scenarios and shocks to assess policymakers in the ex-ante assessment of policy options and market conditions. The findings of this study provide valuable insights into the interplay between the water sector and the Jordanian economy and can inform policy decisions related to water resource management and development.

Analyzing the interactions between spot and time charter freight is crucial for the maritime industry. While numerous studies have explored the relationship between average freight indices and spillover effects, a gap remains in understanding the deeper connections between inter-regional shipping routes and chartering contracts. This research investigates the role of Capesize freight dynamics in shaping the regional dry bulk freight market, with a focus on the influence of energy and commodity price fluctuations. Utilizing the TVP-VAR model, we identify distinct trends across various investment horizons. The analysis reveals that short-term spillovers dominate the system, with crude oil serving as a consistent shock transmitter within the time charter network. The China-Brazil route drives spillovers across all periods, while the Australia-China route transitions from absorbing short-term volatility to transmitting long-term shocks. Similarly, the Tubarão-Rotterdam and Bolivar-Rotterdam routes display comparable shifts, transmitting short-term spillovers but absorbing long-term volatility. These findings offer valuable insights for stakeholders seeking to manage risks amidst economic and geopolitical uncertainties.

Abstract

The Household Income, Expenditure and Consumption Survey (HIECS) is of great importance among other household surveys conducted by statistical agencies in various countries around the world. This survey provides a large amount of data to rely on in measuring the living standards of households and individuals, as well as establishing databases that serve in measuring poverty, designing social assistance programs, and providing necessary weights to compile consumer price indices, considered to be an important indicator to assess inflation. The first survey that covered all the country governorates was carried out in 1958/1959 followed by a long series of similar surveys. The current survey, HIECS 2012/2013, is the eleventh in this long series. Starting 2008/2009, Household Income, Expenditure and Consumption Surveys were conducted each two years instead of five years. This would enable better tracking of the rapid changes in the level of the living standards of the Egyptian households. CAPMAS started in 2010/2011 to follow a panel sample of around 40% of the total household sample size. The current survey is the second one to follow a panel sample. This procedure will provide the necessary data to extract accurate indicators on the status of the society. The CAPMAS also is pleased to disseminate the results of this survey to policy makers, researchers and scholarly to help in policy making and conducting development related researches and studies The survey main objectives are: - To identify expenditure levels and patterns of population as well as socio- economic and demographic differentials. - To measure average household and per-capita expenditure for various expenditure items along with socio-economic correlates. - To Measure the change in living standards and expenditure patterns and behavior for the individuals and households in the panel sample, previously surveyed in 2008/2009, for the first time during 12 months representing the survey period. - To define percentage distribution of expenditure for various items used in compiling consumer price indices which is considered important indicator for measuring inflation. - To estimate the quantities, values of commodities and services consumed by households during the survey period to determine the levels of consumption and estimate the current demand which is important to predict future demands. - To define average household and per-capita income from different sources. - To provide data necessary to measure standard of living for households and individuals. Poverty analysis and setting up a basis for social welfare assistance are highly dependent on the results of this survey. - To provide essential data to measure elasticity which reflects the percentage change in expenditure for various commodity and service groups against the percentage change in total expenditure for the purpose of predicting the levels of expenditure and consumption for different commodity and service items in urban and rural areas. - To provide data essential for comparing change in expenditure against change in income to measure income elasticity of expenditure. - To study the relationships between demographic, geographical, housing characteristics of households and their income. - To provide data necessary for national accounts especially in compiling inputs and outputs tables. - To identify consumers behavior changes among socio-economic groups in urban and rural areas. - To identify per capita food consumption and its main components of calories, proteins and fats according to its nutrition components and the levels of expenditure in both urban and rural areas. - To identify the value of expenditure for food according to its sources, either from household production or not, in addition to household expenditure for non-food commodities and services. - To identify distribution of households according to the possession of some appliances and equipments such as (cars, satellites, mobiles ,…etc) in urban and rural areas that enables measuring household wealth index. - To identify the percentage distribution of income earners according to some background variables such as housing conditions, size of household and characteristics of head of household. - To provide a time series of the most important data related to dominant standard of living from economic and social perspective. This will enable conducting comparisons based on the results of these time series. In addition to, the possibility of performing geographical comparisons.

Compared to previous surveys, the current survey experienced certain peculiarities, among which : 1) The total sample of the current survey (24.9 thousand households) is divided into two sections: a -A new sample of 16.1 thousand households. This sample was used to study the geographic differences between urban governorates, urban and rural areas, and frontier governorates as well as other discrepancies related to households characteristics and household size, head of the household's education status, etc.
b -A panel sample of 2008/2009 survey data of around 8.8 thousand households were selected to accurately study the changes that may have occurred in the households' living standards over the period between the two surveys and over time in the future since CAPMAS will continue to collect panel data for HIECS in the coming years.

2) Some additional questions that showed to be important based on previous surveys results, were added to the survey questionnaire, such as: a - The extent of health services provided to monitor the level of services available in the Egyptian society. By collecting information on the in-kind transfers, the household received during the year; in order to monitor the assistance the household received from different sources government, association,..etc. b - Identifying the main outlet of fabrics, clothes and footwear to determine the level of living standards of the household.

3) Quality control procedures especially for fieldwork are increased, to ensure data accuracy and avoid any errors in suitable time, as well as taking all the necessary measures to guarantee that mistakes are not repeated, with the application of the principle of reward and punishment.

Geographic coverage

National coverage, covering a sample of urban and rural areas in all the governorates.

Analysis unit

• Household/family
• Individual/person

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of HIECS 2012/2013 is a self-weighted two-stage stratified cluster sample, of around 24.9 households. The main elements of the sampling design are described in the following:

Sample Size The sample has been proportionally distributed on the governorate level between urban and rural areas, in order to make the sample representative even for small governorates. Thus, a sample of about 24863 households has been considered, and was distributed between urban and rural with the percentages of 45.4 % and 54.6, respectively. This sample is divided into two parts: a) A new sample of 16094 households selected from main enumeration areas. b) A panel sample of 8769 households (selected from HIECS 2010/2011 and the preceding survey in 2008/2009).

Cluster Size The cluster size in the previous survey has been decreased compared to older surveys since large cluster sizes previously used were found to be too large to yield accepted design effect estimates (DEFT). As a result, it has been decided to use a cluster size of only 8 households (In HIECS 2011/2012 a cluster size of 16 households was used). While the cluster size for the panel sample was 4 households.

Core Sample The core sample is the master sample of any household sample required to be pulled for the purpose of studying the properties of individuals and families. It is a large sample and distributed on urban and rural areas of all governorates. It is a representative sample for the individual characteristics of the Egyptian society. This sample was implemented in January 2012 and its size reached more than 1 million household (1004800 household) selected from 5024 enumeration areas distributed on all governorates (urban/rural) proportionally with the sample size (the enumeration area size is around 200 households). The core sample is the sampling frame from which the samples for the surveys conducted by CAPMAS are pulled, such as the Labor Force Surveys, Income, Expenditure And Consumption Survey, Household Urban Migration Survey, ...etc, in addition to other samples that may be required for outsources.

New Households Sample: 1000 sample areas were selected across all governorates (urban/rural) using a proportional technique with the sample size. The number required for each governorate (urban/rural) was selected from the enumeration areas of the core sample using a systematic sampling technique.A more detailed description of the different sampling stages and allocation of sample across governorates is provided in the Methodology document available among external resources in Arabic.

Sampling deviation

Given the sample design, these weights will vary to some extent for the over-sampled governorates compared with the others. It is also important to calculate measures of sampling variability for key survey estimates.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different questionnaires have been designed as following: 1) Expenditure and Consumption Questionnaire. 2) Diary Questionnaire (Assisting questionnaire).

In general, foreign direct investments (FDIs) play a crucial role in driving a country’s economic development, promoting diversification, and enhancing competitiveness. The Gulf Cooperation Council (GCC) countries, which heavily rely on the oil and gas sectors, are particularly vulnerable to fluctuations in commodity prices. However, these countries have recognized the imperative of economic diversification and have increasingly turned to inward FDIs to achieve it. By attracting capital, advanced technology, and expertise from foreign investors, FDIs enable the GCC countries to expand their economic base beyond the oil and gas sectors. This diversification not only creates employment opportunities but also fosters resilient economic growth, ultimately leading to an improvement in the living standards of the local population. This study investigates the macroeconomic and environmental factors that potentially attract foreign direct investment (FDI) inflows into the Gulf Cooperation Council (GCC) countries in the long run. Additionally, the study explores the causal relationship between these factors and FDI inflows. The panel autoregressive distributed lag (ARDL) approach to co-integration is the primary analytical technique used, utilizing long time-series data from six GCC countries, including Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates (UAE) during the period 1990–2019. The empirical results indicate that, in the long run, almost all independent variables significantly influence FDI in GCC countries. Variables such as GDP growth (GDPG), inflation (INFL), carbon dioxide emissions (CO2), and urbanization (URB) are found to be highly significant (p≤0.01) in their impact on FDI. Moreover, unemployment (UNEMP) also positively and significantly influences FDI in these countries in the long run. Based on the key findings, strategies aimed at reducing persistently high unemployment rates, maintaining population growth, viewing FDI as a driver for GDP growth, and continuing with infrastructure development and urbanization are expected to attract more FDI inflows into GCC countries in the long run. Additionally, fostering both long-term economic incentives and creating a conducive business infrastructure for investors are vital for attracting inward FDI into any nation, including those in the GCC. This research would benefit various stakeholders, including governments, local businesses, investors, academia, and the local society, by providing valuable knowledge and informing decision-making processes related to economic development, diversification, and investment promotion.

All regressions are done using ordinary least squares regressions on log-transformed variables. The low correlations are likely the result of the incomplete data available for ancient sea, river, and road networks.