Analysis of ‘Dataset of worldwide GDP during Covid Pandemic2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mdjafrilalamshihab/dataset-of-worldwide-gdp-during-covid-pandemic2020 on 28 January 2022.

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

This file contains worldwide GDP information during corona pandemic . The dataset shows the information of GDP of 2020 .

~First column is the name of the countries . ~Second column contains the Nominal GDP per capita ~Third column of the dataset contains the PPP GDP per capita ~Fourth column contains GDP growth rate per capita (in percentage) . Negative indicates that GDP has fallen . ~Last column contains the decision if GDP has fallen or arise.

What can be done by this Dataset? ~Prediction condition GDP during any pandemic like Covid-19 based on this dataset.

Currency used - US Dollar($)

Banner Credit - Mainly taken from y-axis.com

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

505 Economics is on a mission to make academic economics accessible. We've developed the first monthly sub-national GDP data for EU and UK regions from January 2015 onwards.

Our GDP dataset uses luminosity as a proxy for GDP. The brighter a place, the more economic activity that place tends to have.

We produce the data using high-resolution night time satellite imagery and Artificial Intelligence.

This builds on our academic research at the London School of Economics, and we're producing the dataset in collaboration with the European Space Agency BIC UK.

We have published peer-reviewed academic articles on the usage of luminosity as an accurate proxy for GDP.

Key features:

• Granular: Data is provided at the following geographical units:
  • NUTS3 (e.g. Provinces),
  • NUTS2 (e.g. Regions),
  • NUTS1 (e.g. Macroregions), and
  • NUTS0 (e.g. Italy) levels.
• Frequent: Data is provided every month from January 2015. This is more frequent than the annualised official datasets.
• Timely: Data is provided with a one month lag (i.e. the data for January 2021 was published at the end of February 2021). This is substantially quicker than the 18 month lag of official datasets.
• Accurate: Our dataset uses Deep Learning to maximise accuracy (RMSE 1.2%).

The dataset can be used by:

• Governments and policy makers - to monitor the performance of local economies, to measure the localised impact of policies, and to get a real-time indication of economic activity.
• Financial services - to get an indication of national-level GDP before official GDP statistics are released
• Engineering companies - to monitor and evaluate the localised impact of infrastructure projects
• Consultancies - to forecast the localised impact of specific projects, to retrospectively monitor and evaluate the localised impact of existing projects
• Economics firms - to create macro forecasts at the national and sub-national level, to assess the impact of policy interventions.
• Academia / Think Tanks - to conduct novel research at the local level. E.g. our dataset can be used to measure the impact of localised COVID-19 lockdowns.

We have created this dataset for all UK sub-national regions, 28 EU Countries and Switzerland.

Analysis of ‘Expenditure on education as % of GDP’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-opendata-euskadi-eus-catalogo-estadistica-gasto-en-educacion-como-del-pib- on 17 January 2022.

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

The main objective of the Battery of Governance Indicators is to collect economic, social, political-policy and governance indicators that allow government action to be associated with the concept of governance.

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

Analysis of ‘Country, Regional and World GDP’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/tunguz/country-regional-and-world-gdp on 28 January 2022.

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

Read me

Country, regional and world GDP in current US Dollars ($). Regional means collections of countries e.g. Europe & Central Asia.

Data

The data is sourced from the World Bank, which in turn lists as sources: World Bank national accounts data, and OECD National Accounts data files.

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

This measure reports the change rate for the Iowa Real Gross Domestic Product quarterly estimate.

505 Economics is on a mission to make academic economics accessible. We've developed the first monthly sub-national GDP data for EU and UK regions from January 2015 onwards.

Our GDP dataset uses luminosity as a proxy for GDP. The brighter a place, the more economic activity that place tends to have.

We produce the data using high-resolution night time satellite imagery and Artificial Intelligence.

This builds on our academic research at the London School of Economics, and we're producing the dataset in collaboration with the European Space Agency BIC UK.

We have published peer-reviewed academic articles on the usage of luminosity as an accurate proxy for GDP.

Key features:

• Granular: Data is provided at the following geographical units:
  • NUTS3 (e.g. London Boroughs),
  • NUTS2 (e.g. London),
  • NUTS1 (e.g. England), and
  • NUTS0 (e.g. United Kingdom) levels.
• Frequent: Data is provided every month from January 2015. This is more frequent than the annualised official datasets.
• Timely: Data is provided with a one month lag (i.e. the data for January 2021 was published at the end of February 2021). This is substantially quicker than the 18 month lag of official datasets.
• Accurate: Our dataset uses Deep Learning to maximise accuracy (RMSE 1.2%).

The dataset can be used by:

• Governments and policy makers - to monitor the performance of local economies, to measure the localised impact of policies, and to get a real-time indication of economic activity.
• Financial services - to get an indication of national-level GDP before official GDP statistics are released
• Engineering companies - to monitor and evaluate the localised impact of infrastructure projects
• Consultancies - to forecast the localised impact of specific projects, to retrospectively monitor and evaluate the localised impact of existing projects
• Economics firms - to create macro forecasts at the national and sub-national level, to assess the impact of policy interventions.
• Academia / Think Tanks - to conduct novel research at the local level. E.g. our dataset can be used to measure the impact of localised COVID-19 lockdowns.

We have created this dataset for all UK sub-national regions, 28 EU Countries and Switzerland.

Gross Domestic Product (GDP) is a measure of the economic production which takes place within the geographical boundaries of a province or country. It can be measured at basic prices (by industry) or at market prices.

Analysis of ‘GDP 2015-2019: Finland, Norway, and Sweden’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/carlmcbrideellis/gdp-20152019-finland-norway-and-sweden on 28 January 2022.

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

Description

This is a csv file of the GDP data was obtained from The World Bank website. Hopefully it will be of use in the Tabular Playground Series - Jan 2022 competition.

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

Tourism Monthly Time Series Dataset with Economic and Static Covariates

This dataset, originally sourced from Athanasopoulos et al. (2011), focuses on the tourism industry with a monthly frequency and has been enhanced with economic covariates (e.g., CPI, Inflation Rate, GDP) from official Australian government sources. We also perform some preprocessing to further increase the usability of the dataset with dynamic start dates for each series and static covariates for in-depth time… See the full description on the dataset page: https://huggingface.co/datasets/zaai-ai/time_series_datasets.

GDP: Tianjin: Nankai data was reported at 72,566.248 RMB mn in 2022. This records an increase from the previous number of 66,944.221 RMB mn for 2021. GDP: Tianjin: Nankai data is updated yearly, averaging 53,697.500 RMB mn from Dec 2005 (Median) to 2022, with 18 observations. The data reached an all-time high of 89,310.333 RMB mn in 2018 and a record low of 19,530.000 RMB mn in 2005. GDP: Tianjin: Nankai data remains active status in CEIC and is reported by Tianjin Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AI: Gross Domestic Product: Municipality District.

Gross domestic product (GDP), in current dollars, evaluated at basic price for all provinces and territories. These estimates are derived from the provincial Supply and Use Tables.

This dataset is part of a research that seeks to understand the relationship between AI development (measured through the annual publications related to AI) and democracy scores. The dataset includes other economic, political and demographic indicators [GDP Per Capita- Industrialization Index- Natural Resources Revenues- Political System- Fragile States Index -Ethnic Fractionalization -Religion]

Gross Domestic Product (GDP) at basic prices, by North American Industry Classification System (NAICS) aggregates, by Industry, volume measures, monthly, 5 most recent time periods.

Analysis of ‘Gross domestic product: production approach’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/18544115-bundesamt-fur-statistik-bfs on 08 January 2022.

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

This dataset presents the annual figures for the production account and the gross domestic product as well as their variation, since 1995. Descriptions of the variables in the CSV file are available in the Appendix.

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

This table contains 52 series, with data for years 1947 - 2009 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: United States ...) Components (15 items: Total gross national product; Total gross domestic product; Gross domestic product; business; Gross domestic product; business; non-farm ...) Prices (4 items: Current prices; 1992 constant prices; Chained (2000) dollars; Chained (1996) dollars ...).

505 Economics is on a mission to make academic economics accessible. We've developed the first monthly sub-national GDP data for EU and UK regions from January 2015 onwards.

Our GDP dataset uses luminosity as a proxy for GDP. The brighter a place, the more economic activity that place tends to have.

We produce the data using high-resolution night time satellite imagery and Artificial Intelligence.

This builds on our academic research at the London School of Economics, and we're producing the dataset in collaboration with the European Space Agency BIC UK.

We have published peer-reviewed academic articles on the usage of luminosity as an accurate proxy for GDP.

Key features:

• Granular: Data is provided at the following geographical units:
  • NUTS3 (e.g. Barcelona),
  • NUTS2 (e.g. Cataluna),
  • NUTS1 (e.g. Este), and
  • NUTS0 (e.g. Spain) levels.
• Frequent: Data is provided every month from January 2015. This is more frequent than the annualised official datasets.
• Timely: Data is provided with a one month lag (i.e. the data for January 2021 was published at the end of February 2021). This is substantially quicker than the 18 month lag of official datasets.
• Accurate: Our dataset uses Deep Learning to maximise accuracy (RMSE 1.2%).

The dataset can be used by:

• Governments and policy makers - to monitor the performance of local economies, to measure the localised impact of policies, and to get a real-time indication of economic activity.
• Financial services - to get an indication of national-level GDP before official GDP statistics are released
• Engineering companies - to monitor and evaluate the localised impact of infrastructure projects
• Consultancies - to forecast the localised impact of specific projects, to retrospectively monitor and evaluate the localised impact of existing projects
• Economics firms - to create macro forecasts at the national and sub-national level, to assess the impact of policy interventions.
• Academia / Think Tanks - to conduct novel research at the local level. E.g. our dataset can be used to measure the impact of localised COVID-19 lockdowns.

We have created this dataset for all UK sub-national regions, 28 EU Countries and Switzerland.

Analysis of ‘Public (government and higher education) R&D expenditure as % of GDP’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/jrc-10113-rio_publ_rd_as_gdp on 07 January 2022.

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

R&D expenditure of the public sector (government and higher education) as % of GDP and as share of the total R&D expenditure. Created by filtering the original Eurostat dataset.

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

Analysis of ‘Gross domestic product, long time series’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/18544123-bundesamt-fur-statistik-bfs on 08 January 2022.

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

This dataset presents the annual figures for the gross domestic product and the gross domestic product per capita and their variation, since 1948. Descriptions of the variables in the CSV file are available in the Appendix. Caution in use: The data prior to 1995 has been obtained by retropolation using evolution rates, which have been estimated on the basis on old accounting systems (OECD 1952, ESA 1979, ESA 1995). Therefore, this long time series cannot ensure the conceptual and methodological consistency for the entire period.

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

Historical: GDP indexes, using 1986=100 data seasonally adjusted at annual rates, based on the 1968 System of National Accounts international standards. First quarter of 1947 to second quarter of 1997.

This table contains data for gross domestic product (GDP), in current dollars, for all census metropolitan area and non-census metropolitan areas.