This book surveys big data tools used in macroeconomic forecasting and addresses related econometric issues, including how to capture dynamic relationships among variables; how to select parsimonious models; how to deal with model uncertainty, instability, non-stationarity, and mixed frequency data; and how to evaluate forecasts, among others. Each chapter is self-contained with references, and provides solid background information, while also reviewing the latest advances in the field. Accordingly, the book offers a valuable resource for researchers, professional forecasters, and students of quantitative economics.

This volume explores dynamic factor model specification, asymptotic and finite-sample behavior of parameter estimators, identification, frequentist and Bayesian estimation of the corresponding state space models, and applications.

Big data are part of a paradigm shift that is significantly transforming statistical agencies, processes, and data analysis. While administrative and satellite data are already well established, the statistical community is now experimenting with structured and unstructured human-sourced, process-mediated, and machine-generated big data. The proposed SDN sets out a typology of big data for statistics and highlights that opportunities to exploit big data for official statistics will vary across countries and statistical domains. To illustrate the former, examples from a diverse set of countries are presented. To provide a balanced assessment on big data, the proposed SDN also discusses the key challenges that come with proprietary data from the private sector with regard to accessibility, representativeness, and sustainability. It concludes by discussing the implications for the statistical community going forward.

This book constitutes revised selected papers from the 5th Workshop on Mining Data for Financial Applications, MIDAS 2020, held in conjunction with ECML PKDD 2020, in Ghent, Belgium, in September 2020.* The 8 full and 3 short papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with challenges, potentialities, and applications of leveraging data-mining tasks regarding problems in the financial domain. *The workshop was held virtually due to the COVID-19 pandemic. “Information Extraction from the GDELT Database to Analyse EU Sovereign Bond Markets” and “Exploring the Predictive Power of News and Neural Machine Learning Models for Economic Forecasting” are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This two-volume set, LNCS 13163-13164, constitutes the refereed proceedings of the 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021, together with the first edition of the Symposium on Artificial Intelligence and Neuroscience, ACAIN 2021. The total of 86 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 215 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, neuroscience, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.​

This is an open access book. As a leading role in the global megatrend of scientific innovation, China has been creating a more and more open environment for scientific innovation, increasing the depth and breadth of academic cooperation, and building a community of innovation that benefits all. These endeavors have made new contribution to globalization and creating a community of shared future. With the rapid development of modern economic society, in the process of economic management, informatization has become the mainstream of economic development in the future. At the same time, with the emergence of advanced management technologies such as blockchain technology and big data technology, real market information can be quickly obtained in the process of economic management, which greatly reduces the operating costs of the market economy and effectively enhances the management level of operators, thus contributing to the sustained, rapid and healthy development of the market economy. Under the new situation, the innovative application of economic management research is of great practical significance. 2022 International Conference on Bigdata, Blockchain and Economic Management (ICBBEM 2022) will be held on March 25–27, 2022 in Wuhan, China. ICBBEM 2022 will focus on the latest fields of Bigdata, Blockchain and Economic Management to provide an international platform for experts, professors, scholars and engineers from universities, scientific institutes, enterprises and government-affiliated institutions at home and abroad to share experiences, to expand professional fields, to exchange new ideas face to face, to present research results, and to discuss the key challenging issues and research directions facing the development of this field, with a view to promoting the development and application of theories and technologies in universities and enterprises.