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Economic & Econometric


This page will provide you with access to example use cases to power economic and econometric workflows using Refinitiv APIs. On the right are a series of articles describing use cases in detail complete with code examples, snippets and samples, as well as full jupyter notebooks and source code available on Github. At the bottom of the page you will find links to related APIs, with Overview, Quick Start Guide, full Documentation and Tutorials

Power your Economic & Econometric workflows using our powerful set of APIs and Feeds, available via desktop solutions, SDKs and enterprise level APIs where redistribution of content is required. Our web APIs provide a broad range of language support so you have unlimited integration options. These include Python so you can access all wide set of open source libraries that are available.

Our easy to use web APIs provide access to:

The largest and deepest Economic database available with over 8.5 million active economic indicators and 65 years of history across 175 countries. Interpret market trends, economic cycles, and the impact of world events.

Aggregate data from sector, government, financial and non-financial accounts, GDP and debt markets. 

Microeconomic content Including indicators across housing, energy, automotive, aviation, construction, commodities, labor plus key indicators for sub-national areas.

1,000+ national sources including central banks, central statistical agencies, ministries of finance, trade, and labor, trade associations and research institutes.

1800 economic indicators, with figures released in real time. Value added content includes polling conducted by Reuters

Use cases for economic content include trading, analysis of companies and financial instruments, macro-economic modelling, micro-economic modelling, sector and sub-sector modelling, econometrics, credit, risk assessments and data science.

Use cases including code samples and notebooks for:

  • Access all available auto industry indicators for all countries in the world and see if this can help explain movement in global automakers. In order to do this we use an XGBoost model which is a gradient boosted decision-tree class of algorithm.
  • Use real-time and historical time series for COVID cases and mortality globally and cross reference with myriad economic timeseries to forecast downstream economic impacts of the pandemic
  • Using Holt Winters and monthly expenditure and income data to estimate monthly GDP interpolations