Machine learning Techniques in Economics

Machine learning Techniques in Economics
Author: Atin Basuchoudhary,James T. Bang,Tinni Sen
Publsiher: Springer
Total Pages: 94
Release: 2017-12-28
Genre: Business & Economics
ISBN: 9783319690148

Download Machine learning Techniques in Economics Book in PDF, Epub and Kindle

This book develops a machine-learning framework for predicting economic growth. It can also be considered as a primer for using machine learning (also known as data mining or data analytics) to answer economic questions. While machine learning itself is not a new idea, advances in computing technology combined with a dawning realization of its applicability to economic questions makes it a new tool for economists.

Machine Learning for Economics and Finance in TensorFlow 2

Machine Learning for Economics and Finance in TensorFlow 2
Author: Isaiah Hull
Publsiher: Apress
Total Pages: 368
Release: 2020-11-26
Genre: Computers
ISBN: 1484263723

Download Machine Learning for Economics and Finance in TensorFlow 2 Book in PDF, Epub and Kindle

Work on economic problems and solutions with tools from machine learning. ML has taken time to move into the space of academic economics. This is because empirical work in economics is concentrated on the identification of causal relationships in parsimonious statistical models; whereas machine learning is oriented towards prediction and is generally uninterested in either causality or parsimony. That leaves a gap for both students and professionals in the economics industry without a standard reference. This book focuses on economic problems with an empirical dimension, where machine learning methods may offer something of value. This includes coverage of a variety of discriminative deep learning models (DNNs, CNNs, RNNs, LSTMs, the Transformer Model, etc.), generative machine learning models, random forests, gradient boosting, clustering, and feature extraction. You'll also learn about the intersection of empirical methods in economics and machine learning, including regression analysis, text analysis, and dimensionality reduction methods, such as principal components analysis. TensorFlow offers a toolset that can be used to setup and solve any mathematical model, including those commonly used in economics. This book is structured to teach through a sequence of complete examples, each framed in terms of a specific economic problem of interest or topic. Otherwise complicated content is then distilled into accessible examples, so you can use TensorFlow to solve workhorse models in economics and finance. What You'll Learn Define, train, and evaluate machine learning models in TensorFlow 2 Apply fundamental concepts in machine learning, such as deep learning and natural language processing, to economic and financial problems Solve workhorse models in economics and finance Who This Book Is For Students and data scientists working in the economics industry. Academic economists and social scientists who have an interest in machine learning are also likely to find this book useful.

The Economics of Artificial Intelligence

The Economics of Artificial Intelligence
Author: Ajay Agrawal,Joshua Gans,Avi Goldfarb,Catherine Tucker
Publsiher: University of Chicago Press
Total Pages: 172
Release: 2024-03-05
Genre: Business & Economics
ISBN: 9780226833125

Download The Economics of Artificial Intelligence Book in PDF, Epub and Kindle

A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.

Machine Learning and Artificial Intelligence for Agricultural Economics

Machine Learning and Artificial Intelligence for Agricultural Economics
Author: Chandrasekar Vuppalapati
Publsiher: Springer Nature
Total Pages: 611
Release: 2021-10-04
Genre: Business & Economics
ISBN: 9783030774851

Download Machine Learning and Artificial Intelligence for Agricultural Economics Book in PDF, Epub and Kindle

This book discusses machine learning and artificial intelligence (AI) for agricultural economics. It is written with a view towards bringing the benefits of advanced analytics and prognostics capabilities to small scale farmers worldwide. This volume provides data science and software engineering teams with the skills and tools to fully utilize economic models to develop the software capabilities necessary for creating lifesaving applications. The book introduces essential agricultural economic concepts from the perspective of full-scale software development with the emphasis on creating niche blue ocean products. Chapters detail several agricultural economic and AI reference architectures with a focus on data integration, algorithm development, regression, prognostics model development and mathematical optimization. Upgrading traditional AI software development paradigms to function in dynamic agricultural and economic markets, this volume will be of great use to researchers and students in agricultural economics, data science, engineering, and machine learning as well as engineers and industry professionals in the public and private sectors.

Data Science for Economics and Finance

Data Science for Economics and Finance
Author: Sergio Consoli,Diego Reforgiato Recupero,Michaela Saisana
Publsiher: Springer Nature
Total Pages: 357
Release: 2021
Genre: Application software
ISBN: 9783030668914

Download Data Science for Economics and Finance Book in PDF, Epub and Kindle

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.

Machine Learning for Economics and Finance in TensorFlow 2

Machine Learning for Economics and Finance in TensorFlow 2
Author: Isaiah Hull
Publsiher: Unknown
Total Pages: 0
Release: 2021
Genre: Electronic Book
ISBN: 148426374X

Download Machine Learning for Economics and Finance in TensorFlow 2 Book in PDF, Epub and Kindle

Machine learning has taken time to move into the space of academic economics. This is because empirical research in economics is concentrated on the identification of causal relationships in parsimonious statistical models; whereas machine learning is oriented towards prediction and is generally uninterested in either causality or parsimony. That leaves a gap for students, academics, and professionals who lack a standard reference on machine learning for economics and finance. This book focuses on economic and financial problems with an empirical dimension, where machine learning methods may offer something of value. This includes coverage of a variety of discriminative deep learning models (DNNs, CNNs, LSTMs, and DQNs), generative machine learning models (GANs and VAEs), and tree-based models. It also covers the intersection of empirical methods in economics and machine learning, including regression analysis, natural language processing, and dimensionality reduction. TensorFlow offers a toolset that can be used to define and solve any graph-based model, including those commonly used in economics. This book is structured to teach through a sequence of complete examples, each framed in terms of a specific economic problem of interest or topic. This simplifies otherwise complicated concepts, enabling the reader to solve workhorse theoretical models in economics and finance using TensorFlow. You will: • Define, train, and evaluate machine learning models in TensorFlow 2 • Apply fundamental concepts in machine learning, such as deep learning and natural language processing, to economic and financial problems • Solve theoretical models in economics.

The Essentials of Machine Learning in Finance and Accounting

The Essentials of Machine Learning in Finance and Accounting
Author: Mohammad Zoynul Abedin,M. Kabir Hassan,Petr Hajek,Mohammed Mohi Uddin
Publsiher: Routledge
Total Pages: 259
Release: 2021-06-20
Genre: Business & Economics
ISBN: 9781000394115

Download The Essentials of Machine Learning in Finance and Accounting Book in PDF, Epub and Kindle

• A useful guide to financial product modeling and to minimizing business risk and uncertainty • Looks at wide range of financial assets and markets and correlates them with enterprises’ profitability • Introduces advanced and novel machine learning techniques in finance such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches and applies them to analyze finance data sets • Real world applicable examples to further understanding

Econometrics with Machine Learning

Econometrics with Machine Learning
Author: Felix Chan,László Mátyás
Publsiher: Springer Nature
Total Pages: 385
Release: 2022-09-07
Genre: Business & Economics
ISBN: 9783031151491

Download Econometrics with Machine Learning Book in PDF, Epub and Kindle

This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in ‘big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics? As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice.