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Author: Peter Fuleky Publisher: Springer Nature ISBN: 3030311503 Category : Business & Economics Languages : en Pages : 716
Book Description
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.
Author: Peter Fuleky Publisher: Springer Nature ISBN: 3030311503 Category : Business & Economics Languages : en Pages : 716
Book Description
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.
Author: Publisher: Emerald Group Publishing ISBN: 1785603523 Category : Business & Economics Languages : en Pages : 688
Book Description
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.
Author: Cornelia Hammer Publisher: International Monetary Fund ISBN: 1484318978 Category : Business & Economics Languages : en Pages : 41
Book Description
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.
Author: Apurv Jain Publisher: Academic Press ISBN: 0128191228 Category : Business & Economics Languages : en Pages : 250
Book Description
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
Author: Valerio Bitetta Publisher: Springer Nature ISBN: 3030669815 Category : Computers Languages : en Pages : 161
Book Description
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.
Author: Giuseppe Nicosia Publisher: Springer Nature ISBN: 3030954706 Category : Computers Languages : en Pages : 571
Book Description
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.
Author: Philip Hans Franses Publisher: Cambridge University Press ISBN: 1139952129 Category : Business & Economics Languages : en Pages : 304
Book Description
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.
Author: Peter Fuleky
Author: Peter Fuleky
Author: 
Author: Cornelia Hammer
Author: Apurv Jain
Author: Valerio Bitetta
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Author: Giuseppe Nicosia
Author: Mr.Yan Carriere-Swallow
Author: Philip Hans Franses
Author: Christiaan Heij