PURPOSE (possible uses)

Non-professional investors often try to find an interesting stock among those in an index (such as the Standard and Poor's 500, Nasdaq, etc.). They need only one company, the best, and they don't want to fail (perform poorly). So, the metric to optimize is accuracy, described as:

Accuracy = True Positives / (True Positives + False Positives)

And the predictive model can be a binary classifier.

The data covers the price and volume of shares of 31 NASDAQ companies in the year 2022.

Context

Every data set I found to predict a stock price (investing) aims to find the price for the next day, and only for that stock. But in practical terms, people like to find the best stocks to buy from an index and wait a few days hoping to get an increase in the price of this investment.

Content

Rows are grouped by companies and their age (newest to oldest) on a common date. The first column is the company. The following are the age, market, date (separated by year, month, day, hour, minute), share volume, various traditional prices of that share (close, open, high...), some price and volume statistics and target. The target is mainly defined as 1 when the closing price increases by at least 5% in 5 days (open market days). The target is 0 in any other case.

Complex features and target were made by executing: https://www.kaggle.com/code/luisandresgarcia/202307

Thanks

Many thanks to everyone who participates in scientific papers and Kaggle notebooks related to financial investment.

West Virginia and Kansas had the lowest cost of living across all U.S. states, with composite costs being half of those found in Hawaii. This was according to a composite index that compares prices for various goods and services on a state-by-state basis. In West Virginia, the cost of living index amounted to **** — well below the national benchmark of 100. Virginia— which had an index value of ***** — was only slightly above that benchmark. Expensive places to live included Hawaii, Massachusetts, and California. Housing costs in the U.S. Housing is usually the highest expense in a household’s budget. In 2023, the average house sold for approximately ******* U.S. dollars, but house prices in the Northeast and West regions were significantly higher. Conversely, the South had some of the least expensive housing. In West Virginia, Mississippi, and Louisiana, the median price of the typical single-family home was less than ******* U.S. dollars. That makes living expenses in these states significantly lower than in states such as Hawaii and California, where housing is much pricier. What other expenses affect the cost of living? Utility costs such as electricity, natural gas, water, and internet also influence the cost of living. In Alaska, Hawaii, and Connecticut, the average monthly utility cost exceeded *** U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

This data collection presents a summary of selected producer prices of industrial products in the form of long-term overviews. The statistics of the producer prices of industrial products is, as well as the overall official price statistics, focused on the detection of changes in price. Therefore, their main findings are price indices and not average prices. The "index of producer prices for industrial products" measures on a representative basis the average development of effective selling prices of the mining and manufacturing as well as energy and water produced goods, sold on the domestic market (hence the additional term "domestic sales/Inlandsabsatz"). The index of producer prices for industrial products is calculated and published not only as a single row, but also for a large number of commodity groups on different aggregation levels. The applied structural principles of this calculation are since 1985 the so called “Systematic Nomenclature of Commodities for the Production Statistics / Systematisches Güterverzeichnis für die Produktionsstatistik”, to which also the description of the various index positions corresponds. This systematic replaced the older "Systematic of commodities for the industry statistics (WI) of the base years 1976 and 1980. Data tables in the search and downloadsystem HISTAT (topic: foreign trade / Außenhandel): Study descriptions and data descriptions in HISTAT are only available in German. Data Tables in HISTAT: A. Overviews (1950 – 2005) B. Erzeugerpreise gewerblicher Produkte (Inlandsabsatz): Langfristige Übersichten 1938, 1949 – 2005) (Producer prices of industrial products (domestic sales): long-term overviews) C. Übersichten: Lange Reihen (1950 – 2005) (Overviews: long series) D. Zusammengefasste Übersichten für die Basisjahre 1995 und 2000 (1995 – 2005) (consolidated overview, base years 1995 and 2000) Die vorliegende Datensammlung gibt einen zusammenfassenden Überblick zu ausgewählten Erzeugerpreisen gewerblicher Produkte in Form von langfristigen Übersichten. Die Statistik der Erzeugerpreise gewerblicher Produkte ist, wie die gesamte amtliche Preisstatistik, auf den Nachweis von Preisveränderungen ausgerichtet. Daher sind ihre wichtigsten Ergebnisse Preisindizes und Preismesszahlen und nicht Durchschnittspreise. Der „Index der Erzeugerpreise gewerblicher Produkte“ misst auf repräsentativer Grundlage die durchschnittliche Entwicklung der effektiven Verkaufspreise der vom Bergbau und Verarbeitenden Gewerbe sowie von der Energie- und Wasserversorgung erzeugten und am Inlandsmarkt abgesetzten Waren (daher der Zusatz „Inlandsabsatz“). Der Index der Erzeugerpreise gewerblicher Produkte wird nicht nur als Gesamtreihe, sondern für eine große Zahl von Gütergruppen verschiedener Aggregationsstufen berechnet und veröffentlicht. Die dabei angewandten Gliederungsprinzipien sind ab dem Basisjahr 1985 die des „Systematischen Güterverzeichnisses für Produktionsstatistiken“ (GP, verschiedene Ausgaben), dem auch die Beschreibung der verschiedenen Indexpositionen entspricht. Diese Systematik löste das ältere „Systematische Warenverzeichnis für die Industriestatistik“ (WI) der Basisjahre 1976 und 1980 ab. Datentabellen in HISTAT: A. Zusammengefasste Übersichten (1950 – 2005) B. Erzeugerpreise gewerblicher Produkte (Inlandsabsatz): Langfristige Übersichten 1938, 1949 – 2005) C. Übersichten: Lange Reihen (1950 – 2005) D. Zusammengefasste Übersichten für die Basisjahre 1995 und 2000 (1995 – 2005) Sources: Publications of the official statistics with descriptions of the producer prices of industrial products (editor = Federal Statistical Office, Wiesbaden). Statistical Yearbooks (Statistische Jahrbücher) of the Federal Republic of Germany. Special volume 17: Prices, issue 2: Prices and Price indices of the industrial products (Fachserie 17, Preise; Reihe 2, Preise und Preisindizes für gewerbliche Produkte - Erzeugerpreise). Publikationen der amtlichen Statistik mit Darstellungen der Erzeugerpreise gewerblicher Produkte (Statistisches Bundesamt Wiesbaden (Hrsg.): Statistische Jahrbücher für die Bundesrepublik Deutschland. Fachserie 17, Preise; Reihe 2, Preise und Preisindizes für gewerbliche Produkte - Erzeugerpreise).

Annual descriptive price statistics for each calendar year 2005 – 2024 for 11 Local Government Districts in Northern Ireland. The statistics include: • Minimum sale price • Lower quartile sale price • Median sale price • Simple Mean sale price • Upper Quartile sale price • Maximum sale price • Number of verified sales Prices are available where at least 30 sales were recorded in the area within the calendar year which could be included in the regression model i.e. the following sales are excluded: • Non Arms-Length sales • sales of properties where the habitable space are less than 30m2 or greater than 1000m2 • sales less than £20,000. Annual median or simple mean prices should not be used to calculate the property price change over time. The quality (where quality refers to the combination of all characteristics of a residential property, both physical and locational) of the properties that are sold may differ from one time period to another. For example, sales in one quarter could be disproportionately skewed towards low-quality properties, therefore producing a biased estimate of average price. The median and simple mean prices are not ‘standardised’ and so the varying mix of properties sold in each quarter could give a false impression of the actual change in prices. In order to calculate the pure property price change over time it is necessary to compare like with like, and this can only be achieved if the ‘characteristics-mix’ of properties traded is standardised. To calculate pure property change over time please use the standardised prices in the NI House Price Index Detailed Statistics file.

Expressed in averages for the year, not end-of-period data. A consumer price index (CPI) measures changes in the prices of goods and services that households consume. Such changes affect the real purchasing power of consumers' incomes and their welfare. As the prices of different goods and services do not all change at the same rate, a price index can only reflect their average movement. A price index is typically assigned a value of unity, or 100, in some reference period and the values of the index for other periods of time are intended to indicate the average proportionate, or percentage, change in prices from this price reference period. Price indices can also be used to measure differences in price levels between different cities, regions or countries at the same point in time. [CPI Manual 2004, Introduction] For euro countries, consumer prices are calculated based on harmonized prices. For more information see http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-BE-04-001/EN/KS-BE-04-001-EN.PDF.]

The Consumer price index (CPI) all households, calculated by Statistics Netherlands, measures the average price changes of goods and services purchased by households. The index is an important criterion for inflation, frequently used by trade and industry, employers' organisations, trade unions and government. The index is for instance, used to make adjustments to wages, tax tablesand index-linked rent increases, annuities, etc.

Data available from: January 1996 till December 2015

Status of the figures: The figures in this table are final.

Changes as of 18 May 2016: None, this table is stopped.

Changes from 7 January 2016: New figures added.

Changes from 10 December 2015: On 1 October 2015, the points system for the pricing of rental homes was adjusted by the Dutch national government. As a direct consequence, rental prices of a limited number of dwellings were reduced, which had a downward effect on the average rental price. The effect of this decrease on the rental price indices and imputed rent value could not be determined in time because housing associations announced the impact of rent adjustments only in November. For this reason, the figures of the groups 04100 ‘Actual rentals for housing’ and 04200 ‘Imputed rent value’ over October 2015 have now been adjusted.

The figures of the groups 061100 ‘Pharmaceutical products’, 061200 ‘Other medical products, equipment’, 072200 ‘Fuels and lubricants’ and 083000 ‘Telephone and internet services’ over the months June through September 2015 have been corrected. This has no impact on the headline indices.

The derived CPI decreased by 0.01 index point over August 2015.

When will new figures be published? Not applicable. This table is succeeded by Consumer prices; price index 2015=100. See paragraph 3.

Annual descriptive price statistics for each calendar year 2005 – 2024 for 462 electoral wards within 11 Local Government Districts. The statistics include: • Minimum sale price • Lower quartile sale price • Median sale price • Simple Mean sale price • Upper Quartile sale price • Maximum sale price • Number of verified sales Prices are available where at least 30 sales were recorded in the area within the calendar year which could be included in the regression model i.e. the following sales are excluded: • Non Arms-Length sales • sales of properties where the habitable space are less than 30m2 or greater than 1000m2 • sales less than £20,000. Annual median or simple mean prices should not be used to calculate the property price change over time. The quality (where quality refers to the combination of all characteristics of a residential property, both physical and locational) of the properties that are sold may differ from one time period to another. For example, sales in one quarter could be disproportionately skewed towards low-quality properties, therefore producing a biased estimate of average price. The median and simple mean prices are not ‘standardised’ and so the varying mix of properties sold in each quarter could give a false impression of the actual change in prices. In order to calculate the pure property price change over time it is necessary to compare like with like, and this can only be achieved if the ‘characteristics-mix’ of properties traded is standardised. To calculate pure property change over time please use the standardised prices in the NI House Price Index Detailed Statistics file.

• Maximum sale price • Number of verified sales Prices are available where at least 30 sales were recorded in the area within the calendar year which could be included in the regression model i.e. the following sales are excluded: • Non Arms-Length sales • sales of properties where the habitable space are less than 30m2 or greater than 1000m2 • sales less than £20,000. Annual median or simple mean prices should not be used to calculate the property price change over time. The quality (where quality refers to the combination of all characteristics of a residential property, both physical and locational) of the properties that are sold may differ from one time period to another. For example, sales in one quarter could be disproportionately skewed towards low-quality properties, therefore producing a biased estimate of average price. The median and simple mean prices are not ‘standardised’ and so the varying mix of properties sold in each quarter could give a false impression of the actual change in prices. In order to calculate the pure property price change over time it is necessary to compare like with like, and this can only be achieved if the ‘characteristics-mix’ of properties traded is standardised. To calculate pure property change over time please use the standardised prices in the NI House Price Index Detailed Statistics file.

This table shows the consumer price index for all households (CPI), split up into an index for frequent "out-of-pocket" purchases (FROOPP) and less frequent or "non-out-of-pocket" purchased items (non-FROOPP). Frequent purchased items are purchases that are typically done at least monthly. Out-of-pocket purchases are those that are considered to be typically paid for by the consumer directly and actively. This table also includes the monthly and yearly price developments.

The FROOPP and non-FROOPP are special extracts of the CPI. The corresponding CPI weights and prices are used to calculate both indices. The segmentation used is derived from the FROOPP-classification of Eurostat.

Data available from: January 2006 till December 2015

Status of the figures: The figures in this table are final.

Changes as of 18 May 2016 None, this table is stopped.

Changes as of 10 December 2015 On 1 October 2015, the points system for the pricing of rental homes was adjusted by the Dutch national government. As a direct consequence, rental prices of a limited number of dwellings were reduced, which had a downward effect on the average rental price. The effect of this decrease on the rental price indices and imputed rent value could not be determined in time because housing associations announced the impact of rent adjustments only in November. For this reason, the figures of the groups 04100 ‘Actual rentals for housing’ and 04200 ‘Imputed rent value’ over October 2015 have now been adjusted.

The figures of the groups 061100 ‘Pharmaceutical products’, 061200 ‘Other medical products, equipment’, 072200 ‘Fuels and lubricants’ and 083000 ‘Telephone and internet services’ over the months June through September 2015 have been corrected. This has no impact on the headline indices.

When will new figures be published? Not applicable.

analyze the consumer expenditure survey (ce) with r the consumer expenditure survey (ce) is the primo data source to understand how americans spend money. participating households keep a running diary about every little purchase over the year. those diaries are then summed up into precise expenditure categories. how else are you gonna know that the average american household spent $34 (±2) on bacon, $826 (±17) on cellular phones, and $13 (±2) on digital e-readers in 2011? an integral component of the market basket calculation in the consumer price index, this survey recently became available as public-use microdata and they're slowly releasing historical files back to 1996. hooray! for a t aste of what's possible with ce data, look at the quick tables listed on their main page - these tables contain approximately a bazillion different expenditure categories broken down by demographic groups. guess what? i just learned that americans living in households with $5,000 to $9,999 of annual income spent an average of $283 (±90) on pets, toys, hobbies, and playground equipment (pdf page 3). you can often get close to your statistic of interest from these web tables. but say you wanted to look at domestic pet expenditure among only households with children between 12 and 17 years old. another one of the thirteen web tables - the consumer unit composition table - shows a few different breakouts of households with kids, but none matching that exact population of interest. the bureau of labor statistics (bls) (the survey's designers) and the census bureau (the survey's administrators) have provided plenty of the major statistics and breakouts for you, but they're not psychic. if you want to comb through this data for specific expenditure categories broken out by a you-defined segment of the united states' population, then let a little r into your life. fun starts now. fair warning: only analyze t he consumer expenditure survey if you are nerd to the core. the microdata ship with two different survey types (interview and diary), each containing five or six quarterly table formats that need to be stacked, merged, and manipulated prior to a methodologically-correct analysis. the scripts in this repository contain examples to prepare 'em all, just be advised that magnificent data like this will never be no-assembly-required. the folks at bls have posted an excellent summary of what's av ailable - read it before anything else. after that, read the getting started guide. don't skim. a few of the descriptions below refer to sas programs provided by the bureau of labor statistics. you'll find these in the C:\My Directory\CES\2011\docs directory after you run the download program. this new github repository contains three scripts: 2010-2011 - download all microdata.R lo op through every year and download every file hosted on the bls's ce ftp site import each of the comma-separated value files into r with read.csv depending on user-settings, save each table as an r data file (.rda) or stat a-readable file (.dta) 2011 fmly intrvw - analysis examples.R load the r data files (.rda) necessary to create the 'fmly' table shown in the ce macros program documentation.doc file construct that 'fmly' table, using five quarters of interviews (q1 2011 thru q1 2012) initiate a replicate-weighted survey design object perform some lovely li'l analysis examples replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using unimputed variables replicate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t -tests using unimputed variables create an rsqlite database (to minimize ram usage) containing the five imputed variable files, after identifying which variables were imputed based on pdf page 3 of the user's guide to income imputation initiate a replicate-weighted, database-backed, multiply-imputed survey design object perform a few additional analyses that highlight the modified syntax required for multiply-imputed survey designs replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using imputed variables repl icate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t-tests using imputed variables replicate the %proc_reg() and %proc_logistic() macros found in "ce macros.sas" and provide some examples of regressions and logistic regressions using both unimputed and imputed variables replicate integrated mean and se.R match each step in the bls-provided sas program "integr ated mean and se.sas" but with r instead of sas create an rsqlite database when the expenditure table gets too large for older computers to handle in ram export a table "2011 integrated mean and se.csv" that exactly matches the contents of the sas-produced "2011 integrated mean and se.lst" text file click here to view these three scripts for...

Historical changes of annual temperature and precipitation indices at selected 210 U.S. cities

This dataset provide:

Annual average temperature, total precipitation, and temperature and precipitation extremes calculations for 210 U.S. cities.

Historical rates of changes in annual temperature, precipitation, and the selected temperature and precipitation extreme indices in the 210 U.S. cities.

Estimated thresholds (reference levels) for the calculations of annual extreme indices including warm and cold days, warm and cold nights, and precipitation amount from very wet days in the 210 cities.

Annual average of daily mean temperature, Tmax, and Tmin are included for annual average temperature calculations. Calculations were based on the compiled daily temperature and precipitation records at individual cities.

Temperature and precipitation extreme indices include: warmest daily Tmax and Tmin, coldest daily Tmax and Tmin , warm days and nights, cold days and nights, maximum 1-day precipitation, maximum consecutive 5-day precipitation, precipitation amounts from very wet days.

Number of missing daily Tmax, Tmin, and precipitation values are included for each city.

Rates of change were calculated using linear regression, with some climate indices applied with the Box-Cox transformation prior to the linear regression.

The historical observations from ACIS belong to Global Historical Climatological Network - daily (GHCN-D) datasets. The included stations were based on NRCC’s “ThreadEx” project, which combined daily temperature and precipitation extremes at 255 NOAA Local Climatological Locations, representing all large and medium size cities in U.S. (See Owen et al. (2006) Accessing NOAA Daily Temperature and Precipitation Extremes Based on Combined/Threaded Station Records).

Resources:

See included README file for more information.

Additional technical details and analyses can be found in: Lai, Y., & Dzombak, D. A. (2019). Use of historical data to assess regional climate change. Journal of climate, 32(14), 4299-4320. https://doi.org/10.1175/JCLI-D-18-0630.1

Other datasets from the same project can be accessed at: https://kilthub.cmu.edu/projects/Use_of_historical_data_to_assess_regional_climate_change/61538

ACIS database for historical observations: http://scacis.rcc-acis.org/

GHCN-D datasets can also be accessed at: https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/

Station information for each city can be accessed at: http://threadex.rcc-acis.org/

• 2024 August updated -

  Annual calculations for 2022 and 2023 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2022 and 2023 data.

  Note that future updates may be infrequent.

• 2022 January updated -

  Annual calculations for 2021 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2021 data.

• 2021 January updated -

  Annual calculations for 2020 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2020 data.

• 2020 January updated -

  Annual calculations for 2019 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2019 data.

  Thresholds for all 210 cities were combined into one single file – Thresholds.csv.

• 2019 June updated -

  Baltimore was updated with the 2018 data (previously version shows NA for 2018) and new ID to reflect the GCHN ID of Baltimore-Washington International AP. city_info file was updated accordingly.

  README file was updated to reflect the use of "wet days" index in this study. The 95% thresholds for calculation of wet days utilized all daily precipitation data from the reference period and can be different from the same index from some other studies, where only days with at least 1 mm of precipitation were utilized to calculate the thresholds. Thus the thresholds in this study can be lower than the ones that would've be calculated from the 95% percentiles from wet days (i.e., with at least 1 mm of precipitation).

Short-term business statistics (STS) give information on a wide range of economic activities. All STS data are index data. Additionally, annual absolute values are released for building permits indicators. Percentage changes are also available for each indicator: Infra-annual percentage changes - changes between two consecutive months or quarters - are calculated on the basis of non-adjusted data (prices) or calendar and seasonally adjusted data (volume and value indicators) and year-on-year changes - comparing a period to the same period one year ago - are calculated on the basis of non-adjusted data (prices and employment) or calendar adjusted data (volume and value indicators).

The index data are generally presented in the following forms:

• unadjusted
• calendar adjusted
• calendar and seasonally adjusted.

Depending on the EBS Regulation data are accessible as monthly, quarterly and annual data.

The STS indicators are listed below in five different sectors, reflecting the dissemination of these data in Eurostat’s online database “Eurobase”.

Based on the national data, Eurostat compiles short-term indicators for the EU and euro area. Among these, a list of indicators, called Principal European Economic Indicators (PEEIs) has been identified by key users as being of primary importance for the conduct of monetary and economic policy of the euro area. The PEEIs contributed by STS are marked with * in the text below.

The euro indicators are released through Eurostat's website.

INDUSTRY

• production (volume)*
• turnover: Total, Domestic market and Non-domestic market. A further breakdown of the non-domestic turnover into euro area and non-euro area is available for the euro area countries.
• producer prices (output prices)*: Total, Domestic market and Non-domestic market. A further breakdown of the non-domestic producer prices into euro area and non-euro area is available for the euro area countries.
• import prices*: Total, euro area market, Non euro area market (euro area countries only)
• labour input indicators: Number of employees and self-employed persons, Hours worked by employees, Gross wages and salaries

CONSTRUCTION

• production (volume)*
• building permits indicators*: number of dwellings, square meters of useful floor
• producer (output) prices in construction (if not available, they can be approximated by the construction costs variable)
• labour input indicators: number of employees and self-employed persons, hours worked by employees, gross wages and salaries

TRADE

• volume of sales (deflated turnover)*
• turnover (value)
• labour input indicators: number of employees and self-employed persons, hours worked by employees, gross wages and salaries

SERVICES

• production (volume)*
• turnover (in value)
• labour input indicators: number of employees and self-employed persons, hours worked by employees, gross wages and salaries
• producer prices (output prices)*

MARKET ECONOMY

• total market production (volume)
• registrations
• bankruptcies

National reference metadata of the reporting countries are available in the Annexes to this metadata file.

In the present data compilation the most important group indices of producer prices of agricultural products in form of long series with different base years will be presented in overviews for selected index positions. The index measures the development of selling prices in agriculture in domestic sales. The reference value of the overall index is the value of sales revenues in agriculture in the base year. In respect of the essential calculating process, the indices can be understood as a weighted average of price changes that are calculated for a representative selection of products and services. The price indices of agricultural products are calculated as the annual averages of the estimated average values on a quarterly basis of the goods with the corresponding quarterly sales in the current base year. The indices are calculated with the so called Laspeyres- formula. This means that the estimated numbers from the base year will be unchanged until the conversion of the indices into a new base year. The indices of the producer prices of agricultural products will not only be published as an overall index, but also for different levels of aggregation (product groups) and for single price representatives. In the present long term overviews only the aggregation concerning product types will be considered. Up to and including 1966/67 the reported indices include the betterment and/or sales tax. Since 1967 the index results appear twice in the publications of the federal statistical office; with and without the generalized betterment tax. In the present data compilation the indices in the tables of producer prices of agricultural products by the year of 1968 will be shown exclusively without betterment or sales tax and without upgrading compensation! Data tables in HISTAT: Index of producer prices of agricultural products: marketing years1950/51 = 100 and original base marketing year 1950/52 base changes to 1938/39=100. Base years 1970, 1976, 1980, 1985, 1991, 1995, 2000 = 100. Register of the tables in HISTAT: A.00a Index of producer prices of agricultural products, original base marketing year 1950/51=100 (1938-1959) A.00b Index of producer prices of agricultural products, original base marketing year 1950/51, base changed to 1938/39 = 100 (1938-1958) A.01 Overview: Index of producer prices of agricultural products, long series, originals base marketing year 1962/63 (1950-1972) A.02 Overview: Index of producer prices of agricultural products, long series, originals base marketing year 1970 (1961-1977) A.03 Overview: Index of producer prices of agricultural products, long series, originals base marketing year 1976 (1961-1981) A.04 Overview: Index of producer prices of agricultural products, long series, originals base marketing year 1980 (1961-1987) A.05 Overview: Index of producer prices of agricultural products, long series, originals base marketing year 1985 (1963-1995) A.06 Overview: Index of producer prices of agricultural products, long series, originals base marketing year 1991 (1970-1999) A.07 Overview: Index of producer prices of agricultural products, long series, originals base marketing year 1995 (1983-2003) A.08 Overview: Index of producer prices of agricultural products, long series, originals base marketing year 2000 (2000-2005)

This table includes figures on the price developments of a package of goods and services purchased by consumers in the Netherlands. The figures are consistent with European directives also known as the harmonised consumer price index (HICP). In all member states of the European Union (EU), these indices are compiled in a similar manner to facilitate comparison between the various EU countries.

This table also contains the HICP at constant taxes: this price index excludes the effect of changes in the rates of product-related taxes (e.g. VAT and excise duty on alcohol and tobacco).

The table also includes the month-on-month and year-on-year changes of the HICP. The year-on-year change of total consumer expenditure is known as inflation. The figures are shown for 327 product groups in 2025. Furthermore, 34 combinations of product groups (special aggregates) are displayed. The weighting coefficient shows how much consumers in the Netherlands spend on each product group in relation to their total expenditure. The total weighting is 100,000.

HICP figures are published every month. In addition, an annual figure is published at the end of the year. The HICP of a calendar year is calculated as the average of the indices of the twelve months of that year.

Data available from: January 1996.

Status of the figures: Figures of the flash estimate are published at the end of a reporting month, or shortly thereafter. At the flash estimate, figures are made available for the all items category and for a selection of special aggregates. These figures are calculated on the basis of still incomplete source data. The results of the flash estimate are characterized as provisional.

In most cases, the figures are final in the second publication of the same reporting month. Differences between the provisional and final indices are caused by source material that has become available after the flash estimate. The results of the HICP are only marked as provisional in the second publication if it is already known at the time of publication that data are still incomplete, a revision is expected in a later month, or in special circumstances such as the corona crisis. In that case, the figures become final one month later.

Changes compared with previous version: Data on the most recent period have been added and/or adjustments have been implemented.

Changes as of 13 February 2025: Starting in the reporting month of January 2025, price changes will be published for expenditure categories 053290 Other small electric household appliances and 103000 Post-secondary non-tertiary education. The base period for this new index series is December 2024. This means that the index level of 100 is the price level measured in December 2024.

Changes as of 8 February 2024: Starting in the reporting month of January 2024, a price change will be published for expenditure category 063000 Hospital Services. The base period for this new index series is December 2023. This means that the index level of 100 is the price level measured in December 2023. Previously, between 2000 and 2009, an index was published for the same expenditure category. The base year for that index series was 2005=100. It was discontinued after December 2009. The current series starts again from 100 in December 2023.

When will new figures be published? The figures of the flash estimate are published on the last working day of the month to which the figures relate, or shortly thereafter.

Final figures will usually be published between the first and second Thursday of the month following on the reporting month.

All CPI and HICP publications are announced on the publication calendar.

The harmonised consumer price index (HICP), calculated by Statistics Netherlands, measures the average price changes of goods and services purchased by households. As the harmonised consumer price index is compiled in a similar way in all member states, it makes it possible to compare price developments within the EU properly. This table also shows the index in which the taxes are constant (HICP -CT, Constant Taxes). Data available from: January 2002 till december 2015 Status of the figures: The figures in this table are final. Changes as of 16 June 2016 None, this table is stopped. Changes as of 18 December 2015 The figures of the groups 061100 ‘Pharmaceutical products’, 061200 ‘Other medical products, equipment’, 072200 ‘Fuels and lubricants’ and 083000 ‘Telephone and internet services’ over the months June through September 2015 have been corrected. This has no impact on the headline indices. Changes as of 10 December 2015 On 1 October 2015, the points system for the pricing of rental homes was adjusted by the Dutch national government. As a direct consequence, rental prices of a limited number of dwellings were reduced, which had a downward effect on the average rental price. The effect of this decrease on the rental price indices and imputed rent value could not be determined in time because housing associations announced the impact of rent adjustments only in November. For this reason, the figure of the group 04100 ‘Actual rentals for housing’ over October 2015 has now been adjusted. When will new figures be published? Not applicable. This table is succeeded by Consumer prices; European harmonised price index 2015 = 100. See paragraph 3.

The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally and by region and country. Calculated each year by the International Food Policy Research Institute (IFPRI), the GHI highlights successes and failures in hunger reduction and provide insights into the drivers of hunger, and food and nutrition security. The 2014 GHI has been calculated for 120 countries for which data on the three component indicators are available and for which measuring hung er is considered most relevant. The GHI calculation excludes some higher income countries because the prevalence of hunger there is very low. The GHI is only as current as the data for its three component indicators. This year's GHI reflects the most recent available country level data for the three component indicators spanning the period 2009 to 2013. Besides the most recent GHI scores, this dataset also contains the GHI scores for four other reference periods- 1990, 1995, 2000, and 2005. A country's GHI score is calculated by averaging the percentage of the population that is undernourished, the percentage of children youn ger than five years old who are underweight, and the percentage of children dying before the age of five. This calculation results in a 100 point scale on which zero is the best score (no hunger) and 100 the worst, although neither of these extremes is reached in practice. The three component indicators used to calculate the GHI scores draw upon data from the following sources: 1. Undernourishment: Updated data from the Food and Agriculture Organization of the United Nations (FAO) were used for the 1990, 1995, 2000, 2005, and 2014GHI scores. Undernourishment data for the 2014 GHI are for 2011-2013. 2. Child underweight: The "child underweight" component indicator of the GHI scores includes the latest additions to the World Health Organization's (WHO) Global Database on Child Growth and Malnutrition, and additional data from the joint data base by the United Nations Children's Fund (UNICEF), WHO and the World Bank; the most recent Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey reports; and statistical tables from UNICEF. For the 2014 GHI, data on child underweight are for the latest year for which data are available in the period 2009-2014. 3. Child mortality: Updated data from the UN Inter-agency Group for Child Mortality Estimation were used for the 1990, 1995, 2000, and 2005, and 2014 GHI scores. For the 2014 GHI, data on child mortality are for 2012. Resources related to 2014 Global Hunger Index

This table includes all price index numbers calculated according to the Harmonised consumer price index (HICP) for the Netherlands, the Euro area and the European Union (EU). In all member states of the EU, these indices are compiled in a similar manner to facilitate comparison between the various EU countries.

The table also includes the harmonised consumer price index for the Euro area. This index figure reflects the average price increase/decrease in the countries which have adopted the euro as their currency. The table also includes the European consumer price index, i.e. the harmonised consumer price index for the member states of the European Union.

HICP figures are published every month. In addition, an annual figure is published at the end of the year. The HICP of a calendar year is calculated as the average of the indices of the twelve months of that year.

Data available from: January 1996.

Status of the figures: The HICP results for the Netherlands in this table are in most cases final immediately upon publication. At that time, the results for the euro area are still based on the flash estimate and are characterized as provisional. A month later, these figures become final.

The results of the HICP are only marked as provisional if it is already known at the time of publication that data are still incomplete, a revision is expected in a later month, or in special circumstances such as the corona crisis.

In most cases, all requested price information is known to Statistics Netherlands when the results are published and no adjustment is made later. However, sometimes certain price information is not available in time and the outcome can be adjusted later. HICP results can then always be revised together with the CPI results, even if they were not published as provisional in the previous month. CPI results are marked as provisional when the index figures are first published, the figures are final the following month.

Changes compared with previous version: Data on the most recent period have been added and/or adjustments have been implemented.

Changes as of 12 August 2025: The figures of the European Union have been revised for May 2025.

When will new figures be published? New figures will usually be published between the first and second Thursday of the month following on the reporting month.

All CPI and HICP publications are announced on the publication calendar.

In der vorliegenden Datenkompilation werden die wichtigsten Gruppenindizes der Erzeugerpreise landwirtschaftlicher Produkte in Form von langen Reihen mit unterschiedlichen Basisjahren in Übersichten für ausgewählte Indexpositionen dargestellt. Der Index misst die Entwicklung der Verkaufspreise der Landwirtschaft beim Absatz im Inland. Die Bezugsgröße des Gesamtindex ist der Wert der Verkaufserlöse der Landwirtschaft im Basisjahr. Im Hinblick auf die wesentlichen Rechenvorgänge können die Indizes als gewogene Durchschnitte aus den Preisveränderungszahlen bezeichnet werden, die für eine repräsentative Auswahl von Produkten bzw. Leistungen gebildet werden. Bei den Preisindizes landwirtschaftlicher Produkte werden die Jahresdurchschnittszahlen durch Wägung der Vierteljahresdurchschnittsmesszahlen der einzelnen Waren mit den entsprechenden Vierteljahresumsätzen im jeweiligen Basisjahr gebildet. Die Indizes werden nach der sog. Laspeyres-Formel berechnet. Das bedeutet, dass die aus dem Basisjahr stammenden Wägungszahlen bis zur Umstellung der Indizes auf eine neues Basisjahr unverändert bleiben. Die Indizes der Erzeugerpreise landwirtschaftlicher Produkte werden nicht nur als Gesamtindex, sondern auch für verschiedene Aggregationsstufen (Produktgruppen) bis hin zu einzelnen Preisrepräsentanten veröffentlicht. In den vorliegenden langfristigen Übersichten wird lediglich die Aggregation nach Produktgruppen berücksichtigt. Bis einschließlich dem Wirtschaftsjahr 1966/67 wurden die Indizes einschl. Umsatz-(Mehrwert-) steuer berichtet. Seit dem Wirtschaftsjahr 1967/68 werden die Indexergebnisse in den Publikationen des Statistischen Bundesamtes doppelt dargestellt, d.h. sowohl ohne als auch einschließlich (pauschalierter) Mehrwertsteuer. In der vorliegenden Datenkompilation werden in den Tabellen der Erzeugerpreise landwirtschaftlicher Produkte die Indizes ab dem Jahr 1968 ausschließlich ohne Umsatz-(Mehrwert-)steuer und ohne Aufwertungsausgleich dargestellt! Datentabellen in HISTAT:Index der Erzeugerpreise landwirtschaftlicher Produkte: Wirtschaftsjahre 1950/51 = 100 und Originalbasis Wirtschaftsjahr 1950/51 = 100, umbasiert auf 1938/39 = 100. Wirtschaftsjahr 1962/63 = 100. Basisjahre 1970, 1976, 1980, 1985, 1991, 1995, 2000 = 100. In the present data compilation the most important group indices of producer prices of agricultural products in form of long series with different base years will be presented in overviews for selected index positions. The index measures the development of selling prices in agriculture in domestic sales.The reference value of the overall index is the value of sales revenues in agriculture in the base year. In respect of the essential calculating process, the indices can be understood as a weighted average of price changes that are calculated for a representative selection of products and services. The price indices of agricultural products are calculated as the annual averages of the estimated average values on a quarterly basis of the goods with the corresponding quarterly sales in the current base year. The indices are calculated with the so called Laspeyres- formula. This means that the estimated numbers from the base year will be unchanged until the conversion of the indices into a new base year.The indices of the producer prices of agricultural products will not only be published as an overall index, but also for different levels of aggregation (product groups) and for single price representatives. In the present long term overviews only the aggregation concerning product types will be considered. Up to and including 1966/67 the reported indices include the betterment and/or sales tax. Since 1967 the index results appear twice in the publications of the federal statistical office; with and without the generalized betterment tax. In the present data compilation the indices in the tables of producer prices of agricultural products by the year of 1968 will be shown exclusively without betterment or sales tax and without upgrading compensation! Data tables in HISTAT:Index of producer prices of agricultural products: marketing years1950/51 = 100 and original base marketing year 1950/52 base changes to 1938/39=100. Base years 1970, 1976, 1980, 1985, 1991, 1995, 2000 = 100. Register of the tables in HISTAT: A.00a Index of producer prices of agricultural products, original base marketing year 1950/51=100 (1938-1959)A.00b Index of producer prices of agricultural products, original base marketing year 1950/51, base changed to 1938/39 = 100 (1938-1958)A.01 Overview: Index of producer prices of agricultural products, long series, originals base marketing year 1962/63 (1950-1972)A.02 Overview: Index of producer prices of agricultural products, long series, originals base marketing year 1970 (1961-1977)A.03 Overview: Index of producer prices of agricultural products, long series, originals base marketing year 1976 (1961-1981)A.04 Overview: Index of producer prices of agricultural products, long series, originals base marketing year 1980 (1961-1987)A.05 Overview: Index of producer prices of agricultural products, long series, originals base marketing year 1985 (1963-1995)A.06 Overview: Index of producer prices of agricultural products, long series, originals base marketing year 1991 (1970-1999)A.07 Overview: Index of producer prices of agricultural products, long series, originals base marketing year 1995 (1983-2003)A.08 Overview: Index of producer prices of agricultural products, long series, originals base marketing year 2000 (2000-2005)

Abstract

The Residential Property Price Index (RPPI) for South Africa was compiled by Statistics South Africa in partnership with the South African Reserve Bank and with the support of the International Monetary Fund. The source data for the RPPI are the records of property transactions registered with the Office of the Chief Registrar of Deeds (Deeds office). The RPPI is compiled using internationally accepted methods as outlined in Eurostat's Handbook on Residential Property Price Indices and the IMF's Residential Property Price Index Practical Compilation Guide. These documents are provided with the data. The indices are calculated using a rolling window time dummy hedonic regression model. The purpose of RPPIs is to measure changes in the price of residential properties, such as houses, townhouses and flats, purchased by households. Both new and existing dwellings are covered, independently of their final use and their previous owners. Only market prices are considered, including the price of the land on which residential buildings are located.

Analysis unit

Other

Kind of data

Administrative records

Mode of data collection

Other