Prediction And Causality In Econometrics And Related Topics
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Prediction And Causality In Econometrics And Related Topics
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Author : Nguyen Ngoc Thach
language : en
Publisher: Springer Nature
Release Date : 2021-07-26
Prediction And Causality In Econometrics And Related Topics written by Nguyen Ngoc Thach and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-26 with Technology & Engineering categories.
This book provides the ultimate goal of economic studies to predict how the economy develops—and what will happen if we implement different policies. To be able to do that, we need to have a good understanding of what causes what in economics. Prediction and causality in economics are the main topics of this book's chapters; they use both more traditional and more innovative techniques—including quantum ideas -- to make predictions about the world economy (international trade, exchange rates), about a country's economy (gross domestic product, stock index, inflation rate), and about individual enterprises, banks, and micro-finance institutions: their future performance (including the risk of bankruptcy), their stock prices, and their liquidity. Several papers study how COVID-19 has influenced the world economy. This book helps practitioners and researchers to learn more about prediction and causality in economics -- and to further develop this important research direction.
Partial Identification In Econometrics And Related Topics
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Author : Nguyen Ngoc Thach
language : en
Publisher: Springer Nature
Release Date : 2024-07-31
Partial Identification In Econometrics And Related Topics written by Nguyen Ngoc Thach and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-31 with Technology & Engineering categories.
This book covers data processing techniques, with economic and financial application being the unifying theme. To make proper investments in economy, the authors need to have a good understanding of the future trends: how will demand change, how will prices change, etc. In general, in science, the usual way to make predictions is: to identify a model that best fits the current dynamics, and to use this model to predict the future behavior. In many practical situations—especially in economics—our past experiences are limited. As a result, the authors can only achieve a partial identification. It is therefore important to be able to make predictions based on such partially identified models—which is the main focus of this book. This book emphasizes partial identification techniques, but it also describes and uses other econometric techniques, ranging from more traditional statistical techniques to more innovative ones such as game-theoretic approach, interval techniques, and machine learning. Applications range from general analysis of GDP growth, stock market, and consumer prices to analysis of specific sectors of economics (credit and banking, energy, health, labor, tourism, international trade) to specific issues affecting economy such as ecology, national culture, government regulations, and the existence of shadow economy. This book shows what has been achieved, but even more important are remaining open problems. The authors hope that this book will: inspire practitioners to learn how to apply state-of-the-art techniques, especially techniques of optimal transport statistics, to economic and financial problems, and inspire researchers to further improve the existing techniques and to come up with new techniques for studying economic and financial phenomena. The authors want to thank all the authors for their contributions and all anonymous referees for their thorough analysis and helpful comments. The publication of this book—and organization of the conference at which these papers were presented—was supported: by the Ho Chi Minh University of Banking (HUB), Vietnam, and by the Vingroup Innovation Foundation (VINIF). The authors thank the leadership and staff of HUB and VINIF for providing crucial support.
Financial Econometrics Bayesian Analysis Quantum Uncertainty And Related Topics
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Author : Nguyen Ngoc Thach
language : en
Publisher: Springer Nature
Release Date : 2022-05-28
Financial Econometrics Bayesian Analysis Quantum Uncertainty And Related Topics written by Nguyen Ngoc Thach and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-28 with Technology & Engineering categories.
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.
Methodological Issues In Psychology
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Author : David Trafimow
language : en
Publisher: Taylor & Francis
Release Date : 2023-08-17
Methodological Issues In Psychology written by David Trafimow and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-17 with Psychology categories.
Methodological Issues in Psychology is a comprehensive text that challenges current practice in the discipline and provides solutions that are more useful in contemporary research, both basic and applied. This book begins by equipping the readers with the underlying foundation pertaining to basic philosophical issues addressing theory verification or falsification, distinguishing different levels of theorizing, or hypothesizing, and the assumptions necessary to negotiate between these levels. It goes on to specifically focus on statistical and inferential hypotheses including chapters on how to dramatically improve statistical and inferential practices and how to address the replication crisis. Advances to be featured include the author's own inventions, the a priori procedure and gain-probability diagrams, and a chapter about mediation analyses, which explains why such analyses are much weaker than typically assumed. The book also provides an introductory chapter on classical measurement theory and expands to new concepts in subsequent chapters. The final measurement chapter addresses the ubiquitous problem of small effect sizes in psychology and provides recommendations that directly contradict typical thinking and teaching in psychology, but with the consequence that researchers can enjoy dramatically improved effect sizes. Methodological Issues in Psychology is an invaluable asset for students and researchers of psychology. It will also be of vital interest to social science researchers and students in areas such as management, marketing, sociology, and experimental philosophy.
Decision Making Under Uncertainty With A Special Emphasis On Geosciences And Education
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Author : Laxman Bokati
language : en
Publisher: Springer Nature
Release Date : 2023-03-21
Decision Making Under Uncertainty With A Special Emphasis On Geosciences And Education written by Laxman Bokati and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-21 with Technology & Engineering categories.
This book describes new techniques for making decisions in situations with uncertainty and new applications of decision-making techniques. The main emphasis is on situations when it is difficult to decrease uncertainty. For example, it is very difficult to accurately predict human economic behavior, so in economics, it is very important to take this uncertainty into account when making decisions. Other areas where it is difficult to decrease uncertainty are geosciences and teaching. The book analyzes the general problem of decision making and shows how its results can be applied to economics, geosciences, and teaching. Since all these applications involve computing, the book also shows how these results can be applied to computing, including deep learning and quantum computing. The book is recommended to researchers, practitioners, and students who want to learn more about decision making under uncertainty—and who want to work on remaining challenges.
Decision Making Under Uncertainty And Constraints
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Author : Martine Ceberio
language : en
Publisher: Springer Nature
Release Date : 2023-01-03
Decision Making Under Uncertainty And Constraints written by Martine Ceberio and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-03 with Technology & Engineering categories.
This book shows, on numerous examples, how to make decisions in realistic situations when we have both uncertainty and constraints. In most these situations, the book's emphasis is on the why-question, i.e., on a theoretical explanation for empirical formulas and techniques. Such explanations are important: they help understand why these techniques work well in some cases and not so well in others, and thus, help practitioners decide whether a technique is appropriate for a given situation. Example of applications described in the book ranges from science (biosciences, geosciences, and physics) to electrical and civil engineering, education, psychology and decision making, and religion—and, of course, include computer science, AI (in particular, eXplainable AI), and machine learning. The book can be recommended to researchers and students in these application areas. Many of the examples use general techniques that can be used in other application areas as well, so it is also useful for practitioners and researchers in other areas who are looking for possible theoretical explanations of empirical formulas and techniques.
Noneparametric Methods In Economics And Finance Dependence Causality And Prediction
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Author : Valentyn Panchenko
language : en
Publisher: Rozenberg Publishers
Release Date : 2004
Noneparametric Methods In Economics And Finance Dependence Causality And Prediction written by Valentyn Panchenko and has been published by Rozenberg Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
Algebraic Approach To Data Processing
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Author : Julio C. Urenda
language : en
Publisher: Springer Nature
Release Date : 2022-10-15
Algebraic Approach To Data Processing written by Julio C. Urenda and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-15 with Computers categories.
The book explores a new general approach to selecting—and designing—data processing techniques. Symmetry and invariance ideas behind this algebraic approach have been successful in physics, where many new theories are formulated in symmetry terms. The book explains this approach and expands it to new application areas ranging from engineering, medicine, education to social sciences. In many cases, this approach leads to optimal techniques and optimal solutions. That the same data processing techniques help us better analyze wooden structures, lung dysfunctions, and deep learning algorithms is a good indication that these techniques can be used in many other applications as well. The book is recommended to researchers and practitioners who need to select a data processing technique—or who want to design a new technique when the existing techniques do not work. It is also recommended to students who want to learn the state-of-the-art data processing.
Econometrics For Financial Applications
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Author : Ly H. Anh
language : en
Publisher: Springer
Release Date : 2017-12-18
Econometrics For Financial Applications written by Ly H. Anh and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-18 with Technology & Engineering categories.
This book addresses both theoretical developments in and practical applications of econometric techniques to finance-related problems. It includes selected edited outcomes of the International Econometric Conference of Vietnam (ECONVN2018), held at Banking University, Ho Chi Minh City, Vietnam on January 15-16, 2018. Econometrics is a branch of economics that uses mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. An extremely important part of economics is finances: a financial crisis can bring the whole economy to a standstill and, vice versa, a smart financial policy can dramatically boost economic development. It is therefore crucial to be able to apply mathematical techniques of econometrics to financial problems. Such applications are a growing field, with many interesting results – and an even larger number of challenges and open problems.
Machine Learning And Causality The Impact Of Financial Crises On Growth
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Author : Mr.Andrew J Tiffin
language : en
Publisher: International Monetary Fund
Release Date : 2019-11-01
Machine Learning And Causality The Impact Of Financial Crises On Growth written by Mr.Andrew J Tiffin and has been published by International Monetary Fund this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-01 with Computers categories.
Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality. This paper gently introduces some leading work in this area, using a concrete example—assessing the impact of a hypothetical banking crisis on a country’s growth. By enabling consideration of a rich set of potential nonlinearities, and by allowing individually-tailored policy assessments, machine learning can provide an invaluable complement to the skill set of economists within the Fund and beyond.