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Title: Estimating the unemployment rate using big data Authors:  Elisa Jorge-Gonzalez - Universidad de La Laguna (Spain) [presenting]
Abstract: In the last decade, the increased availability of open data has awakened interest to develop indicators for many kinds of phenomena, as well as a need for real-time tools that can create economic time series. Google, Amazon or Apple are an example of companies that use the real-time data generated by the customers activities to extract and use knowledge from raw data to make decisions. Estimates and forecasts of the unemployment rate is an important and difficult task for policymakers. Incorporating information from real-time data has been recently shown to improve estimates and short-term forecasts. The main objective is to investigate whether the use of big data can estimate and forecast the unemployment rate in a particular place and moment, thanks to the use and combination of both open data sources and commercial data. Since the methodology is based on open data, this estimation could be applied to any region worldwide.