Predictive Econometrics And Big Data
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Predictive Econometrics And Big Data
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Author : Vladik Kreinovich
language : en
Publisher: Springer
Release Date : 2017-11-30
Predictive Econometrics And Big Data written by Vladik Kreinovich and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-30 with Technology & Engineering categories.
This book presents recent research on predictive econometrics and big data. Gathering edited papers presented at the 11th International Conference of the Thailand Econometric Society (TES2018), held in Chiang Mai, Thailand, on January 10-12, 2018, its main focus is on predictive techniques – which directly aim at predicting economic phenomena; and big data techniques – which enable us to handle the enormous amounts of data generated by modern computers in a reasonable time. The book also discusses the applications of more traditional statistical techniques to econometric problems. Econometrics is a branch of economics that employs mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. It is therefore important to develop data processing techniques that explicitly focus on prediction. The more data we have, the better our predictions will be. As such, these techniques are essential to our ability to process huge amounts of available data.
Macroeconomic Forecasting In The Era Of Big Data
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Author : Peter Fuleky
language : en
Publisher: Springer Nature
Release Date : 2019-11-28
Macroeconomic Forecasting In The Era Of Big Data written by Peter Fuleky and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-28 with Business & Economics categories.
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.
Dynamic Factor Models
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Author : Siem Jan Koopman
language : en
Publisher: Emerald Group Publishing
Release Date : 2016-01-08
Dynamic Factor Models written by Siem Jan Koopman and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-08 with Business & Economics categories.
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 For Twenty First Century Economic Statistics
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Author : Katharine G. Abraham
language : en
Publisher: University of Chicago Press
Release Date : 2022-03-11
Big Data For Twenty First Century Economic Statistics written by Katharine G. Abraham and has been published by University of Chicago Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-11 with Business & Economics categories.
"The measurement infrastructure for the production of economic statistics in the United States largely was established in the middle part of the 20th century. As has been noted by a number of commentators, the data landscape has changed in fundamental ways since this infrastructure was developed. Obtaining survey responses has become increasingly difficult, leading to increased data collection costs and raising concerns about the quality of the resulting data. At the same time, the economy has become more complex and users are demanding ever more timely and granular data. In this new environment, there is increasing interest in alternative sources of data that might allow the economic statistics agencies to better address users' demands for information. Recent years have seen a proliferation of natively digital data that have enormous potential for improving economic statistics. These include item-level transactional data on price and quantity from retail scanners or companies' internal systems, credit card records, bank account records, payroll records and insurance records compiled for private business purposes; data automatically recorded by sensors or mobile devices; and a growing variety of data that can be obtained from websites and social media platforms. Staggering volumes of digital information relevant to measuring and understanding the economy are generated each second by an increasing array of devices that monitor transactions and business processes as well as track the activities of workers and consumers. Incorporating these non-designed Big Data sources into the economic measurement infrastructure holds the promise of allowing the statistical agencies to produce more accurate, more timely and more disaggregated statistics, with lower burden for data providers and perhaps even at lower cost for the statistical agencies. The agencies already have begun to make use of novel data to augment traditional data sources. Modern data science methods for using Big Data have advanced sufficiently to make the more systematic incorporation of these data into official statistics feasible. Indeed, the availability of new sources of data offers the opportunity to redesign the underlying architecture of official statistics. Considering the threats to the current measurement model arising from falling survey response rates, increased survey costs and the growing difficulties of keeping pace with a rapidly changing economy, fundamental changes in the architecture of the statistical system will be necessary to maintain the quality and utility of official statistics. This volume presents cutting edge research on the deployment of big data to solve both existing and novel challenges in economic measurement. The papers in this volume show that it is practical to incorporate big data into the production of economic statistics in real time and at scale. They report on the application of machine learning methods to extract usable new information from large volumes of data. They also lay out the challenges-both technical and operational-to using Big Data effectively in the production of economic statistics and suggest means of overcoming those challenges. Despite these challenges and the significant agenda for research and development they imply, the papers in the volume point strongly toward more systematic and comprehensive incorporation of Big Data to improve official economic statistics in the coming years"--
Large Dimensional Panel Data Econometrics
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Author : Qu Feng
language : en
Publisher: World Scientific Publishing Company
Release Date : 2020
Large Dimensional Panel Data Econometrics written by Qu Feng and has been published by World Scientific Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Business & Economics categories.
This book aims to fill the gap between panel data econometrics textbooks, and the latest development on "big data", especially large-dimensional panel data econometrics. It introduces important research questions in large panels, including testing for cross-sectional dependence, estimation of factor-augmented panel data models, structural breaks in panels and group patterns in panels. To tackle these high dimensional issues, some techniques used in Machine Learning approaches are also illustrated. Moreover, the Monte Carlo experiments, and empirical examples are also utilised to show how to implement these new inference methods. Large-Dimensional Panel Data Econometrics: Testing, Estimation and Structural Changes also introduces new research questions and results in recent literature in this field.
Applied Economic Forecasting Using Time Series Methods
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Author : Eric Ghysels
language : en
Publisher: Oxford University Press
Release Date : 2018
Applied Economic Forecasting Using Time Series Methods written by Eric Ghysels and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Business & Economics categories.
Economic forecasting is a key ingredient of decision making in the public and private sectors. This book provides the necessary tools to solve real-world forecasting problems using time-series methods. It targets undergraduate and graduate students as well as researchers in public and private institutions interested in applied economic forecasting.
Econometrics And Data Science
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Author : Tshepo Chris Nokeri
language : en
Publisher:
Release Date : 2022
Econometrics And Data Science written by Tshepo Chris Nokeri and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Get up to speed on the application of machine learning approaches in macroeconomic research. This book brings together economics and data science. Author Tshepo Chris Nokeri begins by introducing you to covariance analysis, correlation analysis, cross-validation, hyperparameter optimization, regression analysis, and residual analysis. In addition, he presents an approach to contend with multi-collinearity. He then debunks a time series model recognized as the additive model. He reveals a technique for binarizing an economic feature to perform classification analysis using logistic regression. He brings in the Hidden Markov Model, used to discover hidden patterns and growth in the world economy. The author demonstrates unsupervised machine learning techniques such as principal component analysis and cluster analysis. Key deep learning concepts and ways of structuring artificial neural networks are explored along with training them and assessing their performance. The Monte Carlo simulation technique is applied to stimulate the purchasing power of money in an economy. Lastly, the Structural Equation Model (SEM) is considered to integrate correlation analysis, factor analysis, multivariate analysis, causal analysis, and path analysis. After reading this book, you should be able to recognize the connection between econometrics and data science. You will know how to apply a machine learning approach to modeling complex economic problems and others beyond this book. You will know how to circumvent and enhance model performance, together with the practical implications of a machine learning approach in econometrics, and you will be able to deal with pressing economic problems. What You Will Learn Examine complex, multivariate, linear-causal structures through the path and structural analysis technique, including non-linearity and hidden states Be familiar with practical applications of machine learning and deep learning in econometrics Understand theoretical framework and hypothesis development, and techniques for selecting appropriate models Develop, test, validate, and improve key supervised (i.e., regression and classification) and unsupervised (i.e., dimension reduction and cluster analysis) machine learning models, alongside neural networks, Markov, and SEM models Represent and interpret data and models .
Applied Econometrics With R
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Author : Christian Kleiber
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-12-10
Applied Econometrics With R written by Christian Kleiber and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-12-10 with Business & Economics categories.
R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.
Social And Ethical Considerations Of Ai In East Asia And Beyond
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Author : Levi Checketts
language : en
Publisher: Springer Nature
Release Date : 2025-02-17
Social And Ethical Considerations Of Ai In East Asia And Beyond written by Levi Checketts and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-17 with Science categories.
This book is a global reflection on disparate religious and philosophical approaches to questions of AI and the particular ways these questions affect East Asian societies. These chapters originate from a conference held at Hong Kong Baptist University in April 2023 on “Ethical and Social Issues in AI: East Asia and Beyond.” Sections cover Confucianist, Daoist, and Christian reflections on AI ethics. Chapters on broad theoretical questions that AI poses are included as well as those addressing issues in applied AI ethics. This volume appeals to students and researchers working across cultures and traditions on the philosophy of AI technology.
Digitalization And Economic Development
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Author : Mohamed Sami Ben Ali
language : en
Publisher: Taylor & Francis
Release Date : 2022-06-09
Digitalization And Economic Development written by Mohamed Sami Ben Ali and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-09 with Business & Economics categories.
This book analyses the impact of Information and Communication Technologies (ICTs) on economic development. It contains theoretical and empirical studies, including panel studies on various issues facing developing countries, such as education, corruption, economic growth, government expenditure, financial inclusion, foreign direct investment, infrastructure, economic and social welfare, and inequality. Each chapter offers a well-conceived analysis of the most recent trends in both theory and empirics and addresses numerous policy implications related to the different aspects dealt with in the volume. Overall, this book will be an excellent guide for global graduate students at the Master and PhD levels, as well as academics researching various issues related to economic development while analyzing the role of ICTs. It also addresses numerous policy implications related to economic development for policymakers, investors and stakeholders.