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Macroeconomic Forecasting In The Era Of Big Data
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Book Synopsis Macroeconomic Forecasting in the Era of Big Data by : Peter Fuleky
Download or read book Macroeconomic Forecasting in the Era of Big Data written by Peter Fuleky and published by Springer Nature. This book was released on 2019-11-28 with total page 716 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Dynamic Factor Models written by and published by Emerald Group Publishing. This book was released on 2016-01-08 with total page 688 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Big Data written by Cornelia Hammer and published by International Monetary Fund. This book was released on 2017-09-13 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Macroeconomic Forecasting Using Alternative Data by : Apurv Jain
Download or read book Macroeconomic Forecasting Using Alternative Data written by Apurv Jain and published by Academic Press. This book was released on 2020-12-01 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Macroeconomic Forecasting Using Alternative Data: Techniques for Applying Big Data and Machine Learning applies computer science to the demands of macroeconomic forecasting. It is the first book to combine machine learning methods with macroeconomics. By using artificial intelligence and machine learning techniques, it unlocks the increased forecasting accuracy offered by alternative data sources. Through its interdisciplinary approach, readers learn how to use big datasets efficiently and effectively. Combines big data/machine learning with macroeconomic forecasting Explains how alternative data improves forecasting accuracy when controlled for traditional data sources Provides new innovative methods for handling large databases and improving forecasting accuracy
Book Synopsis Mining Data for Financial Applications by : Valerio Bitetta
Download or read book Mining Data for Financial Applications written by Valerio Bitetta and published by Springer Nature. This book was released on 2021-01-14 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Big Data written by and published by . This book was released on 2011 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Machine Learning, Optimization, and Data Science by : Giuseppe Nicosia
Download or read book Machine Learning, Optimization, and Data Science written by Giuseppe Nicosia and published by Springer Nature. This book was released on 2022-02-01 with total page 571 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Time Series Models for Business and Economic Forecasting by : Philip Hans Franses
Download or read book Time Series Models for Business and Economic Forecasting written by Philip Hans Franses and published by Cambridge University Press. This book was released on 2014-04-24 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: With a new author team contributing decades of practical experience, this fully updated and thoroughly classroom-tested second edition textbook prepares students and practitioners to create effective forecasting models and master the techniques of time series analysis. Taking a practical and example-driven approach, this textbook summarises the most critical decisions, techniques and steps involved in creating forecasting models for business and economics. Students are led through the process with an entirely new set of carefully developed theoretical and practical exercises. Chapters examine the key features of economic time series, univariate time series analysis, trends, seasonality, aberrant observations, conditional heteroskedasticity and ARCH models, non-linearity and multivariate time series, making this a complete practical guide. Downloadable datasets are available online.
Book Synopsis The Economics and Implications of Data by : Mr.Yan Carriere-Swallow
Download or read book The Economics and Implications of Data written by Mr.Yan Carriere-Swallow and published by International Monetary Fund. This book was released on 2019-09-23 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: This SPR Departmental Paper will provide policymakers with a framework for studying changes to national data policy frameworks.
Book Synopsis Macroeconomic Forecasting with Real-time Data by : Christiaan Heij
Download or read book Macroeconomic Forecasting with Real-time Data written by Christiaan Heij and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Data Science for Economics and Finance by : Sergio Consoli
Download or read book Data Science for Economics and Finance written by Sergio Consoli and published by Springer Nature. This book was released on 2021 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
Book Synopsis Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022) by : Daowen Qiu
Download or read book Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022) written by Daowen Qiu and published by Springer Nature. This book was released on 2022-12-28 with total page 1730 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Exploring the Boundaries of Big Data by : Bart van der Sloot
Download or read book Exploring the Boundaries of Big Data written by Bart van der Sloot and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the investigation Exploring the Boundaries of Big Data The Netherlands Scientific Council for Government Policy (WRR) offers building blocks for developing a regulatory approach to Big Data.
Book Synopsis Alternative Economic Indicators by : C. James Hueng
Download or read book Alternative Economic Indicators written by C. James Hueng and published by W.E. Upjohn Institute. This book was released on 2020-09-08 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Policymakers and business practitioners are eager to gain access to reliable information on the state of the economy for timely decision making. More so now than ever. Traditional economic indicators have been criticized for delayed reporting, out-of-date methodology, and neglecting some aspects of the economy. Recent advances in economic theory, econometrics, and information technology have fueled research in building broader, more accurate, and higher-frequency economic indicators. This volume contains contributions from a group of prominent economists who address alternative economic indicators, including indicators in the financial market, indicators for business cycles, and indicators of economic uncertainty.
Book Synopsis Proceedings of the Future Technologies Conference (FTC) 2021, Volume 1 by : Kohei Arai
Download or read book Proceedings of the Future Technologies Conference (FTC) 2021, Volume 1 written by Kohei Arai and published by Springer Nature. This book was released on 2021-10-23 with total page 1020 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers a wide range of important topics including but not limited to Technology Trends, Computing, Artificial Intelligence, Machine Vision, Communication, Security, e-Learning, and Ambient Intelligence and their applications to the real world. The sixth Future Technologies Conference 2021 was organized virtually and received a total of 531 submissions from academic pioneering researchers, scientists, industrial engineers, and students from all over the world.. After a double-blind peer review process, 191 submissions have been selected to be included in these proceedings. One of the meaningful and valuable dimensions of this conference is the way it brings together a large group of technology geniuses in one venue to not only present breakthrough research in future technologies, but also to promote discussions and debate of relevant issues, challenges, opportunities and research findings. We hope that readers find the book interesting, exciting, and inspiring; it provides the state-of-the-art intelligent methods and techniques for solving real-world problems along with a vision of the future research.
Book Synopsis Big Data for Twenty-First-Century Economic Statistics by : Katharine G. Abraham
Download or read book Big Data for Twenty-First-Century Economic Statistics written by Katharine G. Abraham and published by University of Chicago Press. This book was released on 2022-03-11 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction.Big data for twenty-first-century economic statistics: the future is now /Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, and Matthew D. Shapiro --Toward comprehensive use of big data in economic statistics.Reengineering key national economic indicators /Gabriel Ehrlich, John Haltiwanger, Ron S. Jarmin, David Johnson, and Matthew D. Shapiro ;Big data in the US consumer price index: experiences and plans /Crystal G. Konny, Brendan K. Williams, and David M. Friedman ;Improving retail trade data products using alternative data sources /Rebecca J. Hutchinson ;From transaction data to economic statistics: constructing real-time, high-frequency, geographic measures of consumer spending /Aditya Aladangady, Shifrah Aron-Dine, Wendy Dunn, Laura Feiveson, Paul Lengermann, and Claudia Sahm ;Improving the accuracy of economic measurement with multiple data sources: the case of payroll employment data /Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christopher Kurz --Uses of big data for classification.Transforming naturally occurring text data into economic statistics: the case of online job vacancy postings /Arthur Turrell, Bradley Speigner, Jyldyz Djumalieva, David Copple, and James Thurgood ;Automating response evaluation for franchising questions on the 2017 economic census /Joseph Staudt, Yifang Wei, Lisa Singh, Shawn Klimek, J. Bradford Jensen, and Andrew Baer ;Using public data to generate industrial classification codes /John Cuffe, Sudip Bhattacharjee, Ugochukwu Etudo, Justin C. Smith, Nevada Basdeo, Nathaniel Burbank, and Shawn R. Roberts --Uses of big data for sectoral measurement.Nowcasting the local economy: using Yelp data to measure economic activity /Edward L. Glaeser, Hyunjin Kim, and Michael Luca ;Unit values for import and export price indexes: a proof of concept /Don A. Fast and Susan E. Fleck ;Quantifying productivity growth in the delivery of important episodes of care within the Medicare program using insurance claims and administrative data /John A. Romley, Abe Dunn, Dana Goldman, and Neeraj Sood ;Valuing housing services in the era of big data: a user cost approach leveraging Zillow microdata /Marina Gindelsky, Jeremy G. Moulton, and Scott A. Wentland --Methodological challenges and advances.Off to the races: a comparison of machine learning and alternative data for predicting economic indicators /Jeffrey C. Chen, Abe Dunn, Kyle Hood, Alexander Driessen, and Andrea Batch ;A machine learning analysis of seasonal and cyclical sales in weekly scanner data /Rishab Guha and Serena Ng ;Estimating the benefits of new products /W. Erwin Diewert and Robert C. Feenstra.
Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Frank Hutter
Download or read book Machine Learning and Knowledge Discovery in Databases written by Frank Hutter and published by Springer Nature. This book was released on 2021-02-24 with total page 783 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.
