Economic and fiscal policies conceived by international organizations, governments, and central banks heavily depend on economic forecasts, in particular during times of economic and societal turmoil like the one we have recently experienced with the coronavirus spreading world-wide. The accuracy of economic forecasting and nowcasting models is however still problematic since modern economies are subject to numerous shocks that make the forecasting and nowcasting tasks extremely hard, both in the short and medium-long runs. In this context, the use of recent Data Science technologies for improving forecasting and nowcasting for several types of economic and financial applications has high potentials. The vast amount of data available in current times, referred to as Big Data era, opens a huge amount of opportunities to economists and scientists, with a condition that data are opportunately handled, processed, linked, and analyzed. From forecasting economic indexes with little observations and only a few of variables, we now have millions of observations and hundreds of variables. Questions that previously could only be answered with a delay of several months or even years can now be addressed nearly in real time. Big data, related analysis performed through (Deep) Machine Learning technologies, and the availability of more and more performing hardware (Cloud Computing infrastructures, GPUs, etc.) can integrate and augment the information carried out by publicly available aggregated variables produced by national and international statistical agencies. By lowering the level of granularity, Data Science technologies can uncover economic relationships that are often not evident when variables are in an aggregated form over many products, individuals or time periods. Strictly linked to that, the evolution of ICT has contributed to the development of several decision-making instruments that help investors in taking decisions. This evolution also brought about the development of FinTech, a newly coined abbreviation for Financial Technology, whose aim is to leverage cutting-edge technologies to compete with traditional financial methods for the delivery of financial services. This book is inspirired by the desire for stimulating the adoption of Data Science solutions for Economics and Finance, giving a comprehensive picture on the use of Data Science as a new scientific and technological paradigm for boosting these sectors. As a result, the book explores a wide sprectrum of essential aspects of Data Science, spanning from its main concepts, evolution, technical challenges and infrastractures, to its role and vast opportunities it offers in the economic and financial areas. In addition, the book shows some successful applications on advanced Data Science solutions used to extract new knowledge from data in order to improve economic forecasting and nowcasting models. The theme of the book is at the frontier of economic research in academia, statistical agencies, and central banks. Also, in the last couple of years, several master's programs in Data Science and Economics have appeared in top European and international institutions and
universities. Therefore, considering the number of recent initiatives that are now pushing towards the use of data analysis within the economic field, we are pursuing with the present book at highlighting successful applications of Data Science and Artificial Intelligence into to the economic and financial sectors.
CONSOLI Sergio;
REFORGIATO RECUPERO Diego;
SAISANA Michaela;
2021-06-21
Springer Nature
JRC124606
978-3-030-66891-4 (online),
https://www.springer.com/gp/book/9783030668907,
https://publications.jrc.ec.europa.eu/repository/handle/JRC124606,
10.1007/978-3-030-66891-4 (online),
