Beyond Traditional Probabilistic Methods in Economics

Beyond Traditional Probabilistic Methods in Economics

This book presents recent research on probabilistic methods in economics, from machine learning to statistical analysis. Economics is a very important – and at the same a very difficult discipline.

Author: Vladik Kreinovich

Publisher: Springer

ISBN: 3030041999

Category: Computers

Page: 1157

View: 723

This book presents recent research on probabilistic methods in economics, from machine learning to statistical analysis. Economics is a very important – and at the same a very difficult discipline. It is not easy to predict how an economy will evolve or to identify the measures needed to make an economy prosper. One of the main reasons for this is the high level of uncertainty: different difficult-to-predict events can influence the future economic behavior. To make good predictions and reasonable recommendations, this uncertainty has to be taken into account. In the past, most related research results were based on using traditional techniques from probability and statistics, such as p-value-based hypothesis testing. These techniques led to numerous successful applications, but in the last decades, several examples have emerged showing that these techniques often lead to unreliable and inaccurate predictions. It is therefore necessary to come up with new techniques for processing the corresponding uncertainty that go beyond the traditional probabilistic techniques. This book focuses on such techniques, their economic applications and the remaining challenges, presenting both related theoretical developments and their practical applications.
Categories: Computers

Beyond Traditional Probabilistic Methods in Economics

Beyond Traditional Probabilistic Methods in Economics

Beyond Traditional Probabilistic Methods in Econometrics Hung T. Nguyen1,2(B), Nguyen Duc Trung3, and Nguyen Ngoc Thach3 1 Department of Mathematical Sciences, New Mexico State University, Las Cruces, NM 88003, ...

Author: Vladik Kreinovich

Publisher: Springer

ISBN: 9783030042004

Category: Technology & Engineering

Page: 1157

View: 700

This book presents recent research on probabilistic methods in economics, from machine learning to statistical analysis. Economics is a very important – and at the same a very difficult discipline. It is not easy to predict how an economy will evolve or to identify the measures needed to make an economy prosper. One of the main reasons for this is the high level of uncertainty: different difficult-to-predict events can influence the future economic behavior. To make good predictions and reasonable recommendations, this uncertainty has to be taken into account. In the past, most related research results were based on using traditional techniques from probability and statistics, such as p-value-based hypothesis testing. These techniques led to numerous successful applications, but in the last decades, several examples have emerged showing that these techniques often lead to unreliable and inaccurate predictions. It is therefore necessary to come up with new techniques for processing the corresponding uncertainty that go beyond the traditional probabilistic techniques. This book focuses on such techniques, their economic applications and the remaining challenges, presenting both related theoretical developments and their practical applications.
Categories: Technology & Engineering

Data Science for Financial Econometrics

Data Science for Financial Econometrics

Beyond traditional probabilistic methods in econometrics. In: V. Kreinovich, N. Thach, N. Trung, & D. Van Thanh (Eds.) Beyond traditional probabilistic methods in economics. ECONVN 2019. Studies in computational intelligence (Vol. 809).

Author: Nguyen Ngoc Thach

Publisher: Springer Nature

ISBN: 9783030488536

Category: Computers

Page: 633

View: 770

This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models – based on researchers’ insights – can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques.
Categories: Computers

Financial Econometrics

Financial Econometrics

1–20 (2018) Nguyen, T.H., Thach, N.N.: A closer look at the modeling of economics data. In: Kreinovich V., Thach N.N., Trung N.D., Van, T.D. (eds) Beyond Traditional Probabilistic Methods in Economics.

Author: Nguyen Ngoc Thach

Publisher: Springer Nature

ISBN: 9783030986896

Category: Econometrics

Page: 865

View: 837

This book overviews latest ideas and developments in financial econometrics, with an emphasis on how to best use prior knowledge (e.g., Bayesian way) and how to best use successful data processing techniques from other application areas (e.g., from quantum physics). The book also covers applications to economy-related phenomena ranging from traditionally analyzed phenomena such as manufacturing, food industry, and taxes, to newer-to-analyze phenomena such as cryptocurrencies, influencer marketing, COVID-19 pandemic, financial fraud detection, corruption, and shadow economy. This book will inspire practitioners to learn how to apply state-of-the-art Bayesian, quantum, and related techniques to economic and financial problems and inspire researchers to further improve the existing techniques and come up with new techniques for studying economic and financial phenomena. The book will also be of interest to students interested in latest ideas and results.
Categories: Econometrics

Beyond Traditional Probabilistic Data Processing Techniques Interval Fuzzy etc Methods and Their Applications

Beyond Traditional Probabilistic Data Processing Techniques  Interval  Fuzzy etc  Methods and Their Applications

This has become a standard procedure in analyzing economic data: first, we check if after the integration of appropriate order, we get a stationary process, and then we apply stationarity-based statistical methods to the resulting ...

Author: Olga Kosheleva

Publisher: Springer Nature

ISBN: 9783030310417

Category: Computers

Page: 649

View: 743

Data processing has become essential to modern civilization. The original data for this processing comes from measurements or from experts, and both sources are subject to uncertainty. Traditionally, probabilistic methods have been used to process uncertainty. However, in many practical situations, we do not know the corresponding probabilities: in measurements, we often only know the upper bound on the measurement errors; this is known as interval uncertainty. In turn, expert estimates often include imprecise (fuzzy) words from natural language such as "small"; this is known as fuzzy uncertainty. In this book, leading specialists on interval, fuzzy, probabilistic uncertainty and their combination describe state-of-the-art developments in their research areas. Accordingly, the book offers a valuable guide for researchers and practitioners interested in data processing under uncertainty, and an introduction to the latest trends and techniques in this area, suitable for graduate students.
Categories: Computers

Behavioral Predictive Modeling in Economics

Behavioral Predictive Modeling in Economics

Chapman and Hall/ CRC Press, Boca Raton (2019) 18. Nguyen, H.T., Trung, N.D., Thach, N.N.: Beyond traditional probabilistic methods in economics. In: Kreinovich, V., et al. (eds.) Beyond Traditional Probabilistics Methods in ...

Author: Songsak Sriboonchitta

Publisher: Springer Nature

ISBN: 9783030497286

Category: Technology & Engineering

Page: 451

View: 435

This book presents both methodological papers on and examples of applying behavioral predictive models to specific economic problems, with a focus on how to take into account people's behavior when making economic predictions. This is an important issue, since traditional economic models assumed that people make wise economic decisions based on a detailed rational analysis of all the relevant aspects. However, in reality – as Nobel Prize-winning research has shown – people have a limited ability to process information and, as a result, their decisions are not always optimal. Discussing the need for prediction-oriented statistical techniques, since many statistical methods currently used in economics focus more on model fitting and do not always lead to good predictions, the book is a valuable resource for researchers and students interested in the latest results and challenges and for practitioners wanting to learn how to use state-of-the-art techniques.
Categories: Technology & Engineering

Statistical and Fuzzy Approaches to Data Processing with Applications to Econometrics and Other Areas

Statistical and Fuzzy Approaches to Data Processing  with Applications to Econometrics and Other Areas

Beyond traditional probabilistic methods in econometrics, inBeyond Traditional Probabilistic Models in Economics, ed. by V. Kreinovich, et al. Studies in Computational Intelligence 809 (Springer, Cham, 2019) 18.

Author: Vladik Kreinovich

Publisher: Springer Nature

ISBN: 9783030456191

Category: Technology & Engineering

Page: 265

View: 463

Mainly focusing on processing uncertainty, this book presents state-of-the-art techniques and demonstrates their use in applications to econometrics and other areas. Processing uncertainty is essential, considering that computers – which help us understand real-life processes and make better decisions based on that understanding – get their information from measurements or from expert estimates, neither of which is ever 100% accurate. Measurement uncertainty is usually described using probabilistic techniques, while uncertainty in expert estimates is often described using fuzzy techniques. Therefore, it is important to master both techniques for processing data. This book is highly recommended for researchers and students interested in the latest results and challenges in uncertainty, as well as practitioners who want to learn how to use the corresponding state-of-the-art techniques.
Categories: Technology & Engineering

Algebraic Techniques and Their Use in Describing and Processing Uncertainty

Algebraic Techniques and Their Use in Describing and Processing Uncertainty

Beyond Traditional Probabilistic Methods in Economics, pp. 168–175. Springer, Cham, Switzerland (2019) Direct Decompositions of Matrices Adolf Mader and Otto Mutzbauer In Why Bohmian Approach to Quantum Econometrics: An Algebraic .

Author: Hung T. Nguyen

Publisher: Springer Nature

ISBN: 9783030385651

Category: Technology & Engineering

Page: 170

View: 410

This book discusses heuristic methods – methods lacking a solid theoretical justification – which are ubiquitous in numerous application areas, and explains techniques that can make heuristic methods more reliable. Focusing on algebraic techniques, i.e., those that use only a few specific features of a situation, it describes various state-of-the-art applications, ranging from fuzzy methods for dealing with imprecision to general optimization methods and quantum-based methods for analyzing economic phenomena. The book also includes recent results from leading researchers, which could (and hopefully will) provide the basis for future applications. As such, it is a valuable resource for mathematicians interested in potential applications of their algebraic results and ideas, as well as for application specialists wanting to discover how algebraic techniques can help in their domains.
Categories: Technology & Engineering

Credible Asset Allocation Optimal Transport Methods and Related Topics

Credible Asset Allocation  Optimal Transport Methods  and Related Topics

Beyond traditional probabilistic methods in economics. Springer. 25. Kubsch, M., Stamer, I., Steiner, M., Neumann, K., & Parchmann, I. (2021). Beyond p-values: Using Bayesian data analysis in science education research.

Author: Songsak Sriboonchitta

Publisher: Springer Nature

ISBN: 9783030972738

Category: Technology & Engineering

Page: 762

View: 938

This book describes state-of-the-art economic ideas and how these ideas can be (and are) used to make economic decision (in particular, to optimally allocate assets) and to gauge the results of different economic decisions (in particular, by using optimal transport methods). Special emphasis is paid to machine learning techniques (including deep learning) and to different aspects of quantum econometrics—when quantum physics and quantum computing models are techniques are applied to study economic phenomena. Applications range from more traditional economic areas to more non-traditional topics such as economic aspects of tourism, cryptocurrencies, telecommunication infrastructure, and pandemic. This book helps student to learn new techniques, practitioners to become better knowledgeable of the state-of-the-art econometric techniques, and researchers to further develop these important research directions
Categories: Technology & Engineering