Analysis of the CPI benchmark dataset.

Overview with Chart & Report: The Core Consumer Price Index y/y shows changes in prices of a basket of consumer goods and services in the given month compared to the same month of the previous year. Food and energy are excluded

Application of CPI-Pipeline on PubMed dataset.

CPI-DS_IV and CPI-DS_NIV results for the APG kernel pipeline.

Shallow linguistic kernel results on the datasets CPI-DS.

Real wage development

Wage increases do not inevitably lead to an increase in disposable income due to inflation rates. To classify wage development, real wages are first presented, differentiated by type of employment, service group and economic sector. This is then compared with the consumer price index (CPI), both on the basis of the year 2015 and on the basis of 2020. Here, the goods sectors of food and - on the basis of 2015 - also housing rents are presented separately once again.

Base years of the CPI (2015 and 2020)

The consumer price index measures the average price development of all goods and services that private households purchase for consumption purposes. It is a central indicator for assessing the development of the value of money and is used as a benchmark in wage negotiations, for example. With the base year, the calculation modules of the consumer price index (= CPI) on the consumption habits and purchasing behaviour of private households are adjusted to the conditions of the respective year (here: 2015 and 2020). This also includes that the Systematic Schedule of Income and Expenditure of Private Households (abbreviation SEA) is converted and included in its new form in the revision of the CPI. The selection of the individual price representatives forms the determination of the so-called basket of goods with a total of 750 goods. The determination of the weight with which the price development of the individual price representatives are included in the overall index form the weighting scheme. The weighting scheme of the goods and services is recalculated at each revision. The weighting scheme contains the weighting shares for the goods areas of the upper basket level. This weighting information reflects the importance of the individual goods sectors for the population of all goods and services in the respective economic stages. The basis for the recalculation of the weighting scheme is the sample survey of income and consumption, the statistics of the current economic accounts and data of the national accounts. All new weighting schemes refer to the respective base year (i.e. in the case of the present data series to 2015 or 2020) "and remain constant until the next revision in order to be able to represent price developments unaffected by changes in expenditure weights (Laspeyres index)." Ute Egner (2019): Verbraucherpreisstatistik auf neuer Basis 2015. In: Wirtschaft und Statistik (WISTA), 5, 2019, S. 89. In the table, the weighting scheme appears in the table header in the row ´Weighting in 0/00´ (weight in per mille).

To investigate the issue of inflation-hedging to find appropriate hedging assets against inflation by using the VAR or VECM model. We have collected data encompassing housing price indices, stock indices, price indexes, and money supply from five countries: the United States, Hong Kong, South Korea, Singapore, and Taiwan. The housing price index focuses on the transaction prices of listed residential houses in the metropolitan area as the benchmark, the stock price index is the ordinary stock market index of various countries, the price index is the consumer price index (CPI), and the money supply is M2 aggregate. The time period for obtaining data on the housing price index and stock price index is not the same.

Abstract

The main purpose of the Household Income Expenditure Survey (HIES) 2016 was to offer high quality and nationwide representative household data that provided information on incomes and expenditure in order to update the Consumer Price Index (CPI), improve National Accounts statistics, provide agricultural data and measure poverty as well as other socio-economic indicators. These statistics were urgently required for evidence-based policy making and monitoring of implementation results supported by the Poverty Reduction Strategy (I & II), the AfT and the Liberia National Vision 2030. The survey was implemented by the Liberia Institute of Statistics and Geo-Information Services (LISGIS) over a 12-month period, starting from January 2016 and was completed in January 2017. LISGIS completed a total of 8,350 interviews, thus providing sufficient observations to make the data statistically significant at the county level. The data captured the effects of seasonality, making it the first of its kind in Liberia. Support for the survey was offered by the Government of Liberia, the World Bank, the European Union, the Swedish International Development Corporation Agency, the United States Agency for International Development and the African Development Bank. The objectives of the 2016 HIES were:

1. Update the Consumer Price Index (CPI): To obtain a new set of weights for the basket of goods and services that upgrade the Monrovia Consumer Price Index (MCPI) and the National Consumer Price Index (NCPI) and to revise the CPI basket of goods and services in Liberia to reflect the current consumption pattern of residence.
2. Improve National Accounts Statistics: To get information on annual household expenditure patterns in order to update the household component of the National Accounts.
3. Measure Poverty: To prepare robust poverty indices that enable the understanding of poverty dynamics across the country and of the factors influencing them.
4. Improve Agricultural Statistics: To obtain nationally representative and policy relevant agricultural statistics in order to undertake in-depth analysis of agricultural households.
5. Capture Socio-economic Impact of Ebola Virus Disease (EVD): To obtain a post-EVD dataset which allows for an in-depth analysis of the socioeconomic impact of EVD on households.
6. Benchmark Agenda for Transformation Indicators: To provide an update on selected socioeconomic indicators used to benchmark the government’s policies embedded within the Agenda for Transformation.
7. Develop Statistical Capacity: Emphasize capacity building and development of sustainable statistical systems through every stage of the project to produce accurate and timely information about Liberia.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The original sample design for the HIES exploited two-phased clustered sampling methods, encompassing a nationally representative sample of households in every quarter and was obtained using the 2008 National Housing and Population Census sampling frame. The procedures used for each sampling stage are as follows:
i. First stage
Selection of sample EAs. The sample EAs for the 2016 HIES were selected within each stratum systematically with Probability Proportional to Size from the ordered list of EAs in the sampling frame. They are selected separately for each county by urban/rural stratum. The measure of size for each EA was based on the number of households from the sampling frame of EAs based on the 2008 Liberia Census. Within each stratum the EAs were ordered geographically by district, clan and EA codes. This provided implicit geographic stratification of the sampling frame.

ii. Second stage
Selection of sample households within a sample EA. A random systematic sample of 10 households were selected from the listing for each sample EA. Using this type of table, the supervisor only has to look up the total number of households listed, and a specific systematic sample of households is identified in the corresponding row of the table.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were three questionnaires administered for this survey: 1. Household and Individual Questionnaire 2. Market Price Questionnaire 3. Agricultural Recall Questionnaire

Cleaning operations

The data entry clerk for each team, using data entry software called CSPro, entered data for each household in the field. For each household, an error report was generated on-site, which identified key problems with the data collected (outliers, incorrect entries, inconsistencies with skip patterns, basic filters for age and gender specific questions etc.). The Supervisor along with the Data Entry Clerk and the Enumerator that collected the data reviewed these errors. Callbacks were made to households if necessary to verify information and rectify the errors while in that EA.

Once the data were collected in each EA, they were sent to LISGIS headquarters for further processing along with EA reports for each area visited. The HIES Technical committee converted the data into STATA and ran several consistency checks to manage overall data quality and prepared reports to identify key problems with the data set and called the field teams to update them about the same. Monthly reports were prepared by summarizing observations from data received from the field alongside statistics on data collection status to share with the field teams and LISGIS Management.

Compound-protein interaction (CPI) prediction is a foundational task for drug discovery, which process is time-consuming and costly. The effectiveness of CPI prediction can be greatly improved using deep learning methods to accelerate drug development. Large number of recent research results in the field of computer vision, especially in deep learning, have proved that the position, geometry, spatial structure and other features of objects in an image can be well characterized. We propose a novel molecular image-based model named CAT-CPI (combining CNN and transformer to predict CPI) for CPI task. We use Convolution Neural Network (CNN) to learn local features of molecular images and then use transformer encoder to capture the semantic relationships of these features. To extract protein sequence feature, we propose to use a k-gram based method and obtain the semantic relationships of sub-sequences by transformer encoder. In addition, we build a Feature Relearning (FR) module to learn interaction features of compounds and proteins. We evaluated CAT-CPI on three benchmark datasets—Human, Celegans, and Davis—and the experimental results demonstrate that CAT-CPI presents competitive performance against state-of-the-art predictors. In addition, we carry out Drug-Drug Interaction (DDI) experiments to verify the strong potential of the methods based on molecular images and FR module.

BackgroundThe identification of compound-protein interactions (CPIs) is crucial for drug discovery and understanding mechanisms of action. Accurate CPI prediction can elucidate drug-target-disease interactions, aiding in the discovery of candidate compounds and effective synergistic drugs, particularly from traditional Chinese medicine (TCM). Existing in silico methods face challenges in prediction accuracy and generalization due to compound and target diversity and the lack of largescale interaction datasets and negative datasets for model learning.MethodsTo address these issues, we developed a computational model for CPI prediction by integrating the constructed large-scale bioactivity benchmark dataset with a deep learning (DL) algorithm. To verify the accuracy of our CPI model, we applied it to predict the targets of compounds in TCM. An herb pair of Astragalus membranaceus and Hedyotis diffusaas was used as a model, and the active compounds in this herb pair were collected from various public databases and the literature. The complete targets of these active compounds were predicted by the CPI model, resulting in an expanded target dataset. This dataset was next used for the prediction of synergistic antitumor compound combinations. The predicted multi-compound combinations were subsequently examined through in vitro cellular experiments.ResultsOur CPI model demonstrated superior performance over other machine learning models, achieving an area under the Receiver Operating Characteristic curve (AUROC) of 0.98, an area under the precision-recall curve (AUPR) of 0.98, and an accuracy (ACC) of 93.31% on the test set. The model’s generalization capability and applicability were further confirmed using external databases. Utilizing this model, we predicted the targets of compounds in the herb pair of Astragalus membranaceus and Hedyotis diffusaas, yielding an expanded target dataset. Then, we integrated this expanded target dataset to predict effective drug combinations using our drug synergy prediction model DeepMDS. Experimental assay on breast cancer cell line MDA-MB-231 proved the efficacy of the best predicted multi-compound combinations: Combination I (Epicatechin, Ursolic acid, Quercetin, Aesculetin and Astragaloside IV) exhibited a half-maximal inhibitory concentration (IC50) value of 19.41 μM, and a combination index (CI) value of 0.682; and Combination II (Epicatechin, Ursolic acid, Quercetin, Vanillic acid and Astragaloside IV) displayed a IC50 value of 23.83 μM and a CI value of 0.805. These results validated the ability of our model to make accurate predictions for novel CPI data outside the training dataset and evaluated the reliability of the predictions, showing good applicability potential in drug discovery and in the elucidation of the bioactive compounds in TCM.ConclusionOur CPI prediction model can serve as a useful tool for accurately identifying potential CPI for a wide range of proteins, and is expected to facilitate drug research, repurposing and support the understanding of TCM.

Comprehensive dataset on Russia's energy sector, economy, and socio-economic metrics from post-Soviet era to future projections.

This dataset provides a historical and projected overview of key economic, energy, and social indicators for Russia spanning from 1991 (post-Soviet dissolution) to 2025 (including forecasts). It focuses on the oil and gas sector, which has been a cornerstone of Russia's economy, alongside broader macroeconomic and demographic metrics. The data is useful for analyzing trends in energy production, exports, fiscal dependencies, inflation, and social inequality during periods of economic transformation, crises (e.g., 1998 ruble crisis, 2014 sanctions), and recent geopolitical events. Key Features:

Time Coverage: Annual data from 1991 to 2025 (with projections for 2024-2025 based on estimates). Rows: 35 (one per year). Columns: 29, covering energy production, prices, exports, fiscal indicators, demographics, and more. File Format: CSV (UTF-8 encoded for compatibility with special characters like en-dash in tax rates). Data Sources: Compiled from public sources including Rosstat, World Bank, IMF, EIA (U.S. Energy Information Administration), and Russian Central Bank reports. Projections for 2024-2025 are estimates based on trends and may require updates. Missing Values: Some fields (e.g., early years for FDI or import volumes) are blank due to data unavailability; handle with imputation if needed.

Column Descriptions

Githab rep https://github.com/AsDo001/Forecasting-of-revenues-to-the-budget-of-the-Russian-Federation

The 2006 Household Income and Expenditure Survey (2006 HIES) was initiated by Vanuatu National Statistical Office (VNSO) to review its income and expenditure patterns for the national accounts system, to update the Consumer Price Index (CPI) and subsequently revise its Gross Domestic Products (GDP).

Although the 2006 HIES is primarily designed to satisfy the data requirements of the Vanuatu NSO, it is also expected to provide benchmark data for the Millennium Challenge Accounts' (MCA's) infrastructure projects for its impact assessment on the rural economy.

The main objectives of the survey are:

(a) To obtain expenditure weights and other useful data for the up-dating of the basket and weight of the CPI; (b) To supplement the data available for use in compiling official estimates of household accounts in the systems of national accounts; (c) To supply benchmark data needed for assessment for MCA infrastructure projects; (d) To provide data for assessing the impact on household living conditions of existing or proposed economic and social measures, particularly changes in the structure of household expenditures and in household consumption; (e) To supply basic data needed for policy making in connection with social and economic planning; and (f) To gather information on poverty lines and incidence of poverty for determining nutritional level of people.

Version 2.0 - Public use file: Cleaned and anonymized.

In this survey, we intent to determine the number of person in an household, the overall detail income and expenditure of the household. Tho scope also covers the detail information of the persons living in the household. There is a diary which is used to record the daily income and expenditure of the household.

• Collection start: 2006
• Collection end: 2006

Sierra Leone is a country recovering from years of conflict. There has been a great deal of effort placed on rebuilding the statistical institutions and collecting benchmark data for monitoring and evaluating poverty reduction policies. The current household survey provides the data establishing this benchmark using standard household survey questionnaires and provides important information for defining money metric poverty measures. Among the uses of the data, have been: computation of poverty statistics for the PRSP (Poverty Reduction Strategy Paper), fills in gaps in the National Accounts system, and re-base the consumption basket for the CPI (Consumer Price Index). The data will also help in identifying primary target groups for government assistance and forms the basis for rebuilding the capacity of Statistics Sierra Leone (SSL). The specific objectives of the survey are: 1.To provide information on the patterns of household consumption and expenditure disaggregated to the district level. 2. To provide a database for national and regional planning and poverty monitoring. 3. To produce weights required for the revision or construction of the Consumer Price Indices; 4. To provide statistics for the PRSP. 5. To Improve on the estimation of national income. 6. To give an in-depth view into the structure and distribution of wages and conditions of work of the country's labour force, regions, occupations and gender.

SENSEX Index (Annual Closing Value) – Benchmark index of the Bombay Stock Exchange (BSE)

GDP Growth (%) – Annual real GDP growth rates (constant prices)

Inflation Rate (%) – Annual consumer price index (CPI)-based inflation

Exchange Rate (INR/USD) – End-of-year nominal exchange rate

Market Capitalization (INR billion) – Total BSE market value

Trading Volume (Million Shares) – Aggregate trading activity per year

All data have been sourced from official publications including the Reserve Bank of India (RBI), BSE archives, International Monetary Fund (IMF), and World Bank.

The dataset is structured in wide format, with each row representing a calendar year from 1980 to 2024 and each column representing one variable.

The general aim of the Sierra Leone Integrated Household Survey (SLIHS) was to provide statistics on the living conditions of the people of Sierra Leone and to provide inputs to the government of Sierra Leone's policy making process. The study used consumption as the starting measure for household well-being and followed the standard in poverty analysis for developing countries. The SLIHS was prepared as joint work by Statistics Sierra Leone (SSL) and the World Bank Poverty Reduction and Economic Management Unit. SSL had the major responsibility of conducting the survey. The first chapter presents an overview of poverty, demographics, livelihoods, education, and health in Sierra Leone and measures progress in these indicators compared to the 2003 poverty assessment. The work was conducted as part of the poverty update and it included a series of policy notes with more detailed analysis. The objectives of the SLIHS include: 1. Provide benchmark poverty indicators against which the successes of the agenda for change (PRSP II) could be measured. 2. To measure the incidence of poverty alongside other indicators include providing information on patterns of household's consumption and expenditure at a greater level of disaggregation. 3. To provide data for the compilation of national accounts and computation of the Consumer Price Index (CPI) 4. In combination with earlier data this will give a data base for national and state planning and for the estimation of consumption as a proportion of household production. The SLIHS was spread over a 12 month period in order to ensure a continuous recording of household consumption, expenditures and changes occurring thereof in 2011. A total of 9,671 Enumeration areas were selected and about 6,757 households were interviewed all over the country.

Not all app categories can boast the same degree of user retention on day 30. While news apps were reported in the third quarter of 2024 to have a retention rate of almost 10 percent, social media apps presented less than two percent retention rate after 30 days from install. Entertainment apps presented a three percent installation rate, while a shopping apps had a retention rate of around four percent one month after installation. Before retention: user acquisition Gaining new users is fundamental for the healthy growth of a mobile application, and app developers have an array of tools that can be used to expand their audience. As of the second quarter of 2022, CPI, or cost per install, was the most used pricing model for user acquisition campaigns according to app developers worldwide. The cost of acquiring one new install in North America was of 5.28 U.S. dollars, but driving in-app purchases in the region was more pricey, with a cost of roughly 75 U.S. dollars per user. The future of in-app advertising In recent years, subscriptions and in-app purchases have become more popular app monetization practices, with users finally willing to pay for app premium functionalities and services. In 2020, video ads were reportedly the most expensive type of ads to drive conversions on both iOS and Android apps, while banner ads had a cost per action (CPA) of 36.77 U.S. dollars on iOS, and 10.28 U.S. dollars on Android.

From January 2020 through January 2025, the value of money market funds under management in the United Kingdom (UK) remained relatively stable aside from one notable spike in value in October 2022, by January 2023, however, the spike had subsided, and fund levels had fallen below ** million British pounds.

How are money market yields linked to inflation? The money market yields are influenced by inflation expectations. When inflation expectations rise, investors typically demand higher nominal yields to offset the anticipated decline in purchasing power. Market sentiment regarding inflation is reflected in these yields, which act as indicators for both investors and policymakers. The inflation rate for the Consumer Price Index (CPI) in the United Kingdom went from under *** percent in March 2021 to a high of **** percent in October 2022. Although inflation declined to *** percent in October 2023, it remained well above the levels seen before 2021. Consequently, a significant increase in money market yields was observed. Beginning in 2022, the monthly average yields from the British government bonds continued to rise until they reached their peak in mid-2023, indicating higher inflation expectations.

What is LIBOR? The London Interbank Offered Rate, or LIBOR, is a benchmark interest rate that reflects the average interest rate at which major global banks lend to each other in the interbank market. It is used to establish interest rates for financial instruments such as adjustable-rate mortgages, business loans, and derivatives. The six-month overnight London Interbank Offered Rate based on the British pound increased month by month from 2022 onwards, reaching its peak in March 2023 at **** percent. This increase in borrowing costs has a ripple effect throughout the financial system, which means higher interest rates for businesses and consumers overall.