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| data science for economists: Data Science for Economics and Finance Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana, 2021 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. |
| data science for economists: Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions Matt Taddy, 2019-08-23 Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling Understand how use ML tools in real world business problems, where causation matters more that correlation Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science. |
| data science for economists: Quantitative Economics with R Vikram Dayal, 2020-02-03 This book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham’s tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader’s R skills are gradually honed, with the help of “your turn” exercises. At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrap is introduced. Causal inference is illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods—generalized additive models and random forests (an important and versatile machine learning method)—are introduced intuitively with applications. The book will be of great interest to economists—students, teachers, and researchers alike—who want to learn R. It will help economics students gain an intuitive appreciation of applied economics and enjoy engaging with the material actively, while also equipping them with key data science skills. |
| data science for economists: Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi, 2021-05-06 A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data. |
| data science for economists: The Economics of Artificial Intelligence Ajay Agrawal, Joshua Gans, Avi Goldfarb, Catherine Tucker, 2024-03-05 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. |
| data science for economists: Big Data for Twenty-First-Century Economic Statistics Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, Matthew D. Shapiro, 2022-03-11 Introduction.Big data for twenty-first-century economic statistics: the future is now /Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, and Matthew D. Shapiro --Toward comprehensive use of big data in economic statistics.Reengineering key national economic indicators /Gabriel Ehrlich, John Haltiwanger, Ron S. Jarmin, David Johnson, and Matthew D. Shapiro ;Big data in the US consumer price index: experiences and plans /Crystal G. Konny, Brendan K. Williams, and David M. Friedman ;Improving retail trade data products using alternative data sources /Rebecca J. Hutchinson ;From transaction data to economic statistics: constructing real-time, high-frequency, geographic measures of consumer spending /Aditya Aladangady, Shifrah Aron-Dine, Wendy Dunn, Laura Feiveson, Paul Lengermann, and Claudia Sahm ;Improving the accuracy of economic measurement with multiple data sources: the case of payroll employment data /Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christopher Kurz --Uses of big data for classification.Transforming naturally occurring text data into economic statistics: the case of online job vacancy postings /Arthur Turrell, Bradley Speigner, Jyldyz Djumalieva, David Copple, and James Thurgood ;Automating response evaluation for franchising questions on the 2017 economic census /Joseph Staudt, Yifang Wei, Lisa Singh, Shawn Klimek, J. Bradford Jensen, and Andrew Baer ;Using public data to generate industrial classification codes /John Cuffe, Sudip Bhattacharjee, Ugochukwu Etudo, Justin C. Smith, Nevada Basdeo, Nathaniel Burbank, and Shawn R. Roberts --Uses of big data for sectoral measurement.Nowcasting the local economy: using Yelp data to measure economic activity /Edward L. Glaeser, Hyunjin Kim, and Michael Luca ;Unit values for import and export price indexes: a proof of concept /Don A. Fast and Susan E. Fleck ;Quantifying productivity growth in the delivery of important episodes of care within the Medicare program using insurance claims and administrative data /John A. Romley, Abe Dunn, Dana Goldman, and Neeraj Sood ;Valuing housing services in the era of big data: a user cost approach leveraging Zillow microdata /Marina Gindelsky, Jeremy G. Moulton, and Scott A. Wentland --Methodological challenges and advances.Off to the races: a comparison of machine learning and alternative data for predicting economic indicators /Jeffrey C. Chen, Abe Dunn, Kyle Hood, Alexander Driessen, and Andrea Batch ;A machine learning analysis of seasonal and cyclical sales in weekly scanner data /Rishab Guha and Serena Ng ;Estimating the benefits of new products /W. Erwin Diewert and Robert C. Feenstra. |
| data science for economists: Data Science for Public Policy Jeffrey C. Chen, Edward A. Rubin, Gary J. Cornwall, 2021-09-01 This textbook presents the essential tools and core concepts of data science to public officials, policy analysts, and economists among others in order to further their application in the public sector. An expansion of the quantitative economics frameworks presented in policy and business schools, this book emphasizes the process of asking relevant questions to inform public policy. Its techniques and approaches emphasize data-driven practices, beginning with the basic programming paradigms that occupy the majority of an analyst’s time and advancing to the practical applications of statistical learning and machine learning. The text considers two divergent, competing perspectives to support its applications, incorporating techniques from both causal inference and prediction. Additionally, the book includes open-sourced data as well as live code, written in R and presented in notebook form, which readers can use and modify to practice working with data. |
| data science for economists: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students. |
| data science for economists: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry. |
| data science for economists: Econometrics and Data Science Tshepo Chris Nokeri, 2021-10-27 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 Who This Book Is For Beginning and intermediate data scientists, economists, machine learning engineers, statisticians, and business executives |
| data science for economists: Machine-learning Techniques in Economics Atin Basuchoudhary, James T. Bang, Tinni Sen, 2017-12-28 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. |
| data science for economists: How Economics Shapes Science Paula Stephan, 2015-09-07 The beauty of science may be pure and eternal, but the practice of science costs money. And scientists, being human, respond to incentives and costs, in money and glory. Choosing a research topic, deciding what papers to write and where to publish them, sticking with a familiar area or going into something new—the payoff may be tenure or a job at a highly ranked university or a prestigious award or a bump in salary. The risk may be not getting any of that. At a time when science is seen as an engine of economic growth, Paula Stephan brings a keen understanding of the ongoing cost-benefit calculations made by individuals and institutions as they compete for resources and reputation. She shows how universities offload risks by increasing the percentage of non-tenure-track faculty, requiring tenured faculty to pay salaries from outside grants, and staffing labs with foreign workers on temporary visas. With funding tight, investigators pursue safe projects rather than less fundable ones with uncertain but potentially path-breaking outcomes. Career prospects in science are increasingly dismal for the young because of ever-lengthening apprenticeships, scarcity of permanent academic positions, and the difficulty of getting funded. Vivid, thorough, and bold, How Economics Shapes Science highlights the growing gap between the haves and have-nots—especially the vast imbalance between the biomedical sciences and physics/engineering—and offers a persuasive vision of a more productive, more creative research system that would lead and benefit the world. |
| data science for economists: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. |
| data science for economists: Entrepreneurial Economics Alexander Tabarrok, 2002 This intriguing collection is designed to show how economists can play a more active role in designing and directing the nation's social institutions. By taking the task of political economy seriously, the contributors (including some of today's most distinguished economists) reveal the power of economic thought to offer innovative solutions to some of the most difficult problems facing society today. By creating markets where none existed before, the authors propose efficient, reliable, and profitable improvements to current systems of health insurance, financial markets, human organ distribution, judicial practice, bankruptcy and securities regulation, patenting, and transportation. Written in the entrepreneurial spirit, these essays show economics to be an ambitious, dynamic, and far-from-dismal science. |
| data science for economists: How Economics Became a Mathematical Science E. Roy Weintraub, 2002-05-28 In How Economics Became a Mathematical Science E. Roy Weintraub traces the history of economics through the prism of the history of mathematics in the twentieth century. As mathematics has evolved, so has the image of mathematics, explains Weintraub, such as ideas about the standards for accepting proof, the meaning of rigor, and the nature of the mathematical enterprise itself. He also shows how economics itself has been shaped by economists’ changing images of mathematics. Whereas others have viewed economics as autonomous, Weintraub presents a different picture, one in which changes in mathematics—both within the body of knowledge that constitutes mathematics and in how it is thought of as a discipline and as a type of knowledge—have been intertwined with the evolution of economic thought. Weintraub begins his account with Cambridge University, the intellectual birthplace of modern economics, and examines specifically Alfred Marshall and the Mathematical Tripos examinations—tests in mathematics that were required of all who wished to study economics at Cambridge. He proceeds to interrogate the idea of a rigorous mathematical economics through the connections between particular mathematical economists and mathematicians in each of the decades of the first half of the twentieth century, and thus describes how the mathematical issues of formalism and axiomatization have shaped economics. Finally, How Economics Became a Mathematical Science reconstructs the career of the economist Sidney Weintraub, whose relationship to mathematics is viewed through his relationships with his mathematician brother, Hal, and his mathematician-economist son, the book’s author. |
| data science for economists: The Economics of Data, Analytics, and Digital Transformation Bill Schmarzo, Dr. Kirk Borne, 2020-11-30 Build a continuously learning and adapting organization that can extract increasing levels of business, customer and operational value from the amalgamation of data and advanced analytics such as AI and Machine Learning Key Features Master the Big Data Business Model Maturity Index methodology to transition to a value-driven organizational mindset Acquire implementable knowledge on digital transformation through 8 practical laws Explore the economics behind digital assets (data and analytics) that appreciate in value when constructed and deployed correctly Book Description In today's digital era, every organization has data, but just possessing enormous amounts of data is not a sufficient market discriminator. The Economics of Data, Analytics, and Digital Transformation aims to provide actionable insights into the real market discriminators, including an organization's data-fueled analytics products that inspire innovation, deliver insights, help make practical decisions, generate value, and produce mission success for the enterprise. The book begins by first building your mindset to be value-driven and introducing the Big Data Business Model Maturity Index, its maturity index phases, and how to navigate the index. You will explore value engineering, where you will learn how to identify key business initiatives, stakeholders, advanced analytics, data sources, and instrumentation strategies that are essential to data science success. The book will help you accelerate and optimize your company's operations through AI and machine learning. By the end of the book, you will have the tools and techniques to drive your organization's digital transformation. Here are a few words from Dr. Kirk Borne, Data Scientist and Executive Advisor at Booz Allen Hamilton, about the book: Data analytics should first and foremost be about action and value. Consequently, the great value of this book is that it seeks to be actionable. It offers a dynamic progression of purpose-driven ignition points that you can act upon. What you will learn Train your organization to transition from being data-driven to being value-driven Navigate and master the big data business model maturity index Learn a methodology for determining the economic value of your data and analytics Understand how AI and machine learning can create analytics assets that appreciate in value the more that they are used Become aware of digital transformation misconceptions and pitfalls Create empowered and dynamic teams that fuel your organization's digital transformation Who this book is for This book is designed to benefit everyone from students who aspire to study the economic fundamentals behind data and digital transformation to established business leaders and professionals who want to learn how to leverage data and analytics to accelerate their business careers. |
| data science for economists: Cogs and Monsters Diane Coyle, 2021-10-12 How economics needs to change to keep pace with the twenty-first century and the digital economy Digital technology, big data, big tech, machine learning, and AI are revolutionizing both the tools of economics and the phenomena it seeks to measure, understand, and shape. In Cogs and Monsters, Diane Coyle explores the enormous problems—but also opportunities—facing economics today and examines what it must do to help policymakers solve the world’s crises, from pandemic recovery and inequality to slow growth and the climate emergency. Mainstream economics, Coyle says, still assumes people are “cogs”—self-interested, calculating, independent agents interacting in defined contexts. But the digital economy is much more characterized by “monsters”—untethered, snowballing, and socially influenced unknowns. What is worse, by treating people as cogs, economics is creating its own monsters, leaving itself without the tools to understand the new problems it faces. In response, Coyle asks whether economic individualism is still valid in the digital economy, whether we need to measure growth and progress in new ways, and whether economics can ever be objective, since it influences what it analyzes. Just as important, the discipline needs to correct its striking lack of diversity and inclusion if it is to be able to offer new solutions to new problems. Filled with original insights, Cogs and Monsters offers a road map for how economics can adapt to the rewiring of society, including by digital technologies, and realize its potential to play a hugely positive role in the twenty-first century. |
| data science for economists: Getting Started with Data Science Murtaza Haider, 2015-12-14 Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon. |
| data science for economists: Mathematical Economics Kelvin Lancaster, 2012-10-10 Graduate-level text provides complete and rigorous expositions of economic models analyzed primarily from the point of view of their mathematical properties, followed by relevant mathematical reviews. Part I covers optimizing theory; Parts II and III survey static and dynamic economic models; and Part IV contains the mathematical reviews, which range fromn linear algebra to point-to-set mappings. |
| data science for economists: The Economics and Implications of Data Mr.Yan Carriere-Swallow, Mr.Vikram Haksar, 2019-09-23 This SPR Departmental Paper will provide policymakers with a framework for studying changes to national data policy frameworks. |
| data science for economists: Mastering 'Metrics Joshua D. Angrist, Jörn-Steffen Pischke, 2014-12-21 From Joshua Angrist, winner of the Nobel Prize in Economics, and Jörn-Steffen Pischke, an accessible and fun guide to the essential tools of econometric research Applied econometrics, known to aficionados as 'metrics, is the original data science. 'Metrics encompasses the statistical methods economists use to untangle cause and effect in human affairs. Through accessible discussion and with a dose of kung fu–themed humor, Mastering 'Metrics presents the essential tools of econometric research and demonstrates why econometrics is exciting and useful. The five most valuable econometric methods, or what the authors call the Furious Five—random assignment, regression, instrumental variables, regression discontinuity designs, and differences in differences—are illustrated through well-crafted real-world examples (vetted for awesomeness by Kung Fu Panda's Jade Palace). Does health insurance make you healthier? Randomized experiments provide answers. Are expensive private colleges and selective public high schools better than more pedestrian institutions? Regression analysis and a regression discontinuity design reveal the surprising truth. When private banks teeter, and depositors take their money and run, should central banks step in to save them? Differences-in-differences analysis of a Depression-era banking crisis offers a response. Could arresting O. J. Simpson have saved his ex-wife's life? Instrumental variables methods instruct law enforcement authorities in how best to respond to domestic abuse. Wielding econometric tools with skill and confidence, Mastering 'Metrics uses data and statistics to illuminate the path from cause to effect. Shows why econometrics is important Explains econometric research through humorous and accessible discussion Outlines empirical methods central to modern econometric practice Works through interesting and relevant real-world examples |
| data science for economists: The Soulful Science Diane Coyle, 2009-12-07 For many, Thomas Carlyle's put-down of economics as the dismal science rings true--especially in the aftermath of the crash of 2008. But Diane Coyle argues that economics today is more soulful than dismal, a more practical and human science than ever before. The Soulful Science describes the remarkable creative renaissance in economics, how economic thinking is being applied to the paradoxes of everyday life. This revised edition incorporates the latest developments in the field, including the rise of behavioral finance, the failure of carbon trading, and the growing trend of government bailouts. She also discusses such major debates as the relationship between economic statistics and presidential elections, the boundary between private choice and public action, and who is to blame for today's banking crisis. |
| data science for economists: Economic Analysis of the Digital Economy Avi Goldfarb, Shane M. Greenstein, Catherine Tucker, 2015-05-08 There is a small and growing literature that explores the impact of digitization in a variety of contexts, but its economic consequences, surprisingly, remain poorly understood. This volume aims to set the agenda for research in the economics of digitization, with each chapter identifying a promising area of research. Economics of Digitization identifies urgent topics with research already underway that warrant further exploration from economists. In addition to the growing importance of digitization itself, digital technologies have some features that suggest that many well-studied economic models may not apply and, indeed, so many aspects of the digital economy throw normal economics in a loop. Economics of Digitization will be one of the first to focus on the economic implications of digitization and to bring together leading scholars in the economics of digitization to explore emerging research. |
| data science for economists: Data Science John D. Kelleher, Brendan Tierney, 2018-04-13 A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects. |
| data science for economists: Applied Econometrics with R Christian Kleiber, Achim Zeileis, 2008-12-10 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. |
| data science for economists: Economics for the Rest of Us Moshe Adler, 2009-11-17 “Vivid case studies . . . Adler’s frustration with wrongheaded economic thinking is as entertaining as it is thought provoking.” —Publishers Weekly Why do so many contemporary economists consider food subsidies in starving countries, rent control in rich cities, and health insurance everywhere “inefficient”? Why do they feel that corporate executives deserve no less than their multimillion-dollar “compensation” packages and workers no more than their meager wages? Here is a lively and accessible debunking of the two elements that make economics the “science” of the rich: the definition of what is efficient and the theory of how wages are determined. The first is used to justify the cruelest policies, the second grand larceny. Filled with lively examples—from food riots in Indonesia to eminent domain in Connecticut and everyone from Adam Smith to Jeremy Bentham to Larry Summers—Economics for the Rest of Us shows how today’s dominant economic theories evolved, how they explicitly favor the rich over the poor, and why they’re not the only or best options. Written for anyone with an interest in understanding contemporary economic thinking—and why it is dead wrong—Economics for the Rest of Us offers a foundation for a fundamentally more just economic system. “Brilliant.” —David Cay Johnston, Pulitzer Prize–winning and New York Times–bestselling author of It’s Even Worse Than You Think |
| data science for economists: A Brain-Focused Foundation for Economic Science Richard B. McKenzie, 2018-06-06 This book argues that Lionel Robbins’s construction of the economics field’s organizing cornerstone, scarcity—and all that has been derived from it from economists in Robbins’s time to today—no longer can generate general consent among economists. Since Robbins’ Essay, economists have learned more than Robbins and his cohorts could have imagined about human decision making and about the human brain that is the lynchpin of human decision making. This book argues however that behavioral economists and neuroeconomists, in pointing to numerous ways people fall short of perfectly rational decisions (anomalies, biases, and downright errors), have saved conventional economics from such self-contradictions in what could be viewed as a wayward approach. This book posits that the human brain is the ultimate scarce resource, and that a focus on the brain can bring a new foundation for economics and can save the discipline from hostile criticisms from a variety of non-economists (many psychologists). |
| data science for economists: The Economists' Hour Binyamin Appelbaum, 2019-09-03 In this lively and entertaining history of ideas (Liaquat Ahamed, The New Yorker), New York Times editorial writer Binyamin Appelbaum tells the story of the people who sparked four decades of economic revolution. Before the 1960s, American politicians had never paid much attention to economists. But as the post-World War II boom began to sputter, economists gained influence and power. In The Economists' Hour, Binyamin Appelbaum traces the rise of the economists, first in the United States and then around the globe, as their ideas reshaped the modern world, curbing government, unleashing corporations and hastening globalization. Some leading figures are relatively well-known, such as Milton Friedman, the elfin libertarian who had a greater influence on American life than any other economist of his generation, and Arthur Laffer, who sketched a curve on a cocktail napkin that helped to make tax cuts a staple of conservative economic policy. Others stayed out of the limelight, but left a lasting impact on modern life: Walter Oi, a blind economist who dictated to his wife and assistants some of the calculations that persuaded President Nixon to end military conscription; Alfred Kahn, who deregulated air travel and rejoiced in the crowded cabins on commercial flights as the proof of his success; and Thomas Schelling, who put a dollar value on human life. Their fundamental belief? That government should stop trying to manage the economy.Their guiding principle? That markets would deliver steady growth, and ensure that all Americans shared in the benefits. But the Economists' Hour failed to deliver on its promise of broad prosperity. And the single-minded embrace of markets has come at the expense of economic equality, the health of liberal democracy, and future generations. Timely, engaging and expertly researched, The Economists' Hour is a reckoning -- and a call for people to rewrite the rules of the market. A Wall Street Journal Business BestsellerWinner of the Porchlight Business Book Award in Narrative & Biography |
| data science for economists: The Handbook of Historical Economics Alberto Bisin, Giovanni Federico, 2021-04-27 The Handbook of Historical Economics guides students and researchers through a quantitative economic history that uses fully up-to-date econometric methods. The book's coverage of statistics applied to the social sciences makes it invaluable to a broad readership. As new sources and applications of data in every economic field are enabling economists to ask and answer new fundamental questions, this book presents an up-to-date reference on the topics at hand. Provides an historical outline of the two cliometric revolutions, highlighting the similarities and the differences between the two Surveys the issues and principal results of the second cliometric revolution Explores innovations in formulating hypotheses and statistical testing, relating them to wider trends in data-driven, empirical economics |
| data science for economists: Economics Rules Dani Rodrik, 2015 A leading economist trains a lens on his own discipline to uncover when it fails and when it works. |
| data science for economists: Red Plenty Francis Spufford, 2012-02-14 Spufford cunningly maps out a literary genre of his own . . . Freewheeling and fabulous. —The Times (London) Strange as it may seem, the gray, oppressive USSR was founded on a fairy tale. It was built on the twentieth-century magic called the planned economy, which was going to gush forth an abundance of good things that the lands of capitalism could never match. And just for a little while, in the heady years of the late 1950s, the magic seemed to be working. Red Plenty is about that moment in history, and how it came, and how it went away; about the brief era when, under the rash leadership of Khrushchev, the Soviet Union looked forward to a future of rich communists and envious capitalists, when Moscow would out-glitter Manhattan and every Lada would be better engineered than a Porsche. It's about the scientists who did their genuinely brilliant best to make the dream come true, to give the tyranny its happy ending. Red Plenty is history, it's fiction, it's as ambitious as Sputnik, as uncompromising as an Aeroflot flight attendant, and as different from what you were expecting as a glass of Soviet champagne. |
| data science for economists: Foundations of Research in Economics Steven G. Medema, Warren J. Samuels, 1996 In 21 prescriptive rather than descriptive treatments, well known academic economists set out how they think the discipline should be practiced both internally and in relation to other fields and arenas of society. They explore economics as a historical process and as a public science, realism in model buildings, social science, normative and positive aspects, extracting information from data, and worthwhile economics. Annotation copyright by Book News, Inc., Portland, OR. |
| data science for economists: How to Write about Economics and Public Policy Katerina Petchko, 2018-07-12 How to Write about Economics and Public Policy is designed to guide graduate students through conducting, and writing about, research on a wide range of topics in public policy and economics. This guidance is based upon the actual writing practices of professional researchers in these fields and it will appeal to practitioners and students in disciplinary areas such as international economics, macroeconomics, development economics, public finance, policy studies, policy analysis, and public administration. Supported by real examples from professional and student writers, the book helps students understand what is expected of writers in their field and guides them through choosing a topic for research to writing each section of the paper. This book would be equally effective as a classroom text or a self-study resource. - Teaches students how to write about qualitative and quantitative research in public policy and economics in a way that is suitable for academic consumption and that can drive public policy debates - Uses the genre-based approach to writing to teach discipline-appropriate ways of framing problems, designing studies, and writing and structuring content - Includes authentic examples written by students and international researchers from various sub-disciplines of economics and public policy - Contains strategies and suggestions for textual analysis of research samples to give students an opportunity to practice key points explained in the book - Is based on a comprehensive analysis of a research corpus containing 400+ research articles in various areas of public policy and economics |
| data science for economists: The Role of Scientific and Technical Data and Information in the Public Domain National Research Council, Policy and Global Affairs, Board on International Scientific Organizations, Office of International Scientific and Technical Information Programs, Steering Committee on the Role of Scientific and Technical Data and Information in the Public Domain, 2003-08-29 This symposium brought together leading experts and managers from the public and private sectors who are involved in the creation, dissemination, and use of scientific and technical data and information (STI) to: (1) describe and discuss the role and the benefits and costsâ€both economic and otherâ€of the public domain in STI in the research and education context, (2) to identify and analyze the legal, economic, and technological pressures on the public domain in STI in research and education, (3) describe and discuss existing and proposed approaches to preserving the public domain in STI in the United States, and (4) identify issues that may require further analysis. |
| data science for economists: Gender and the Dismal Science Ann Mari May, 2022-07-05 The economics profession is belatedly confronting glaring gender inequality. Women are systematically underrepresented throughout the discipline, and those who do embark on careers in economics find themselves undermined in any number of ways. Women in the field report pervasive biases and barriers that hinder full and equal participation—and these obstacles take an even greater toll on women of color. How did economics become such a boys’ club, and what lessons does this history hold for attempts to achieve greater equality? Gender and the Dismal Science is a groundbreaking account of the role of women during the formative years of American economics, from the late nineteenth century into the postwar period. Blending rich historical detail with extensive empirical data, Ann Mari May examines the structural and institutional factors that excluded women, from graduate education to academic publishing to university hiring practices. Drawing on material from the archives of the American Economic Association along with novel data sets, she details the vicissitudes of women in economics, including their success in writing monographs and placing journal articles, their limitations in obtaining academic positions, their marginalization in professional associations, and other hurdles that the professionalization of the discipline placed in their path. May emphasizes the formation of a hierarchical culture of status seeking that stymied women’s participation and shaped what counts as knowledge in the field to the advantage of men. Revealing the historical roots of the homogeneity of economics, this book sheds new light on why biases against women persist today. |
| data science for economists: Economics--Mathematical Politics Or Science of Diminishing Returns? Alexander Rosenberg, 1992 Economics will never be able to move beyond these vague predictions because it treats human behavior - individual and social - as the product of expectations and preferences - beliefs and desires - the variables that cannot be measured independently of the actual choices we want to predict. These factors, combined with the economist's commitment to the search for equilibrium solutions to theoretical problems, condemn economic theory to permanent predictive weakness. In the end, Rosenberg's analysis is not merely a critique. His aim is to redefine the scope and value of neoclassical theory, suggesting that its character and most important accomplishments need to be correctly understood to defend economics against the charge that it is a science of diminishing returns.--BOOK JACKET. |
| data science for economists: Econometrics Fumio Hayashi, 2011-12-12 The most authoritative and comprehensive synthesis of modern econometrics available Econometrics provides first-year graduate students with a thoroughly modern introduction to the subject, covering all the standard material necessary for understanding the principal techniques of econometrics, from ordinary least squares through cointegration. The book is distinctive in developing both time-series and cross-section analysis fully, giving readers a unified framework for understanding and integrating results. Econometrics covers all the important topics in a succinct manner. All the estimation techniques that could possibly be taught in a first-year graduate course, except maximum likelihood, are treated as special cases of GMM (generalized methods of moments). Maximum likelihood estimators for a variety of models, such as probit and tobit, are collected in a separate chapter. This arrangement enables students to learn various estimation techniques in an efficient way. Virtually all the chapters include empirical applications drawn from labor economics, industrial organization, domestic and international finance, and macroeconomics. These empirical exercises provide students with hands-on experience applying the techniques covered. The exposition is rigorous yet accessible, requiring a working knowledge of very basic linear algebra and probability theory. All the results are stated as propositions so that students can see the points of the discussion and also the conditions under which those results hold. Most propositions are proved in the text. For students who intend to write a thesis on applied topics, the empirical applications in Econometrics are an excellent way to learn how to conduct empirical research. For theoretically inclined students, the no-compromise treatment of basic techniques is an ideal preparation for more advanced theory courses. |
| data science for economists: Essentials of Economics Paul Krugman, Paul R. Krugman, Robin Wells, Kathryn Graddy, 2010-10 Check out preview content for Essentials of Economics here. Essentials of Economics brings the same captivating writing and innovative features of Krugman/Wells to the one-term economics course. Adapted by Kathryn Graddy, it is the ideal text for teaching basic economic principles, with enough real-world applications to help students see the applicability, but not so much detail as to overwhelm them. Watch a video interview of Paul Krugman here. |
| data science for economists: Social and Economic Networks Matthew O. Jackson, 2010-11-01 Networks of relationships help determine the careers that people choose, the jobs they obtain, the products they buy, and how they vote. The many aspects of our lives that are governed by social networks make it critical to understand how they impact behavior, which network structures are likely to emerge in a society, and why we organize ourselves as we do. In Social and Economic Networks, Matthew Jackson offers a comprehensive introduction to social and economic networks, drawing on the latest findings in economics, sociology, computer science, physics, and mathematics. He provides empirical background on networks and the regularities that they exhibit, and discusses random graph-based models and strategic models of network formation. He helps readers to understand behavior in networked societies, with a detailed analysis of learning and diffusion in networks, decision making by individuals who are influenced by their social neighbors, game theory and markets on networks, and a host of related subjects. Jackson also describes the varied statistical and modeling techniques used to analyze social networks. Each chapter includes exercises to aid students in their analysis of how networks function. This book is an indispensable resource for students and researchers in economics, mathematics, physics, sociology, and business. |
| data science for economists: The World in the Model Mary S. Morgan, 2012-09-17 During the last two centuries, the way economic science is done has changed radically: it has become a social science based on mathematical models in place of words. This book describes and analyses that change - both historically and philosophically - using a series of case studies to illuminate the nature and the implications of these changes. It is not a technical book; it is written for the intelligent person who wants to understand how economics works from the inside out. This book will be of interest to economists and science studies scholars (historians, sociologists and philosophers of science). But it also aims at a wider readership in the public intellectual sphere, building on the current interest in all things economic and on the recent failure of the so-called economic model, which has shaped our beliefs and the world we live in. |
EC349-15 Data Science for Economists
The module will introduce students to the meaning of data science, working practically with data in R. Students will learn how to source, manipulate and analyse large data flows, extract …
Data Science for Economists
Fill in the gaps left by traditional econometrics and methods classes. Practical skills that tools that will bene t your thesis and future career. Neglected skills like how to actually nd datasets in the …
Econometric DATA SCIENCE - Massachusetts Institute of …
Econometric Data Science develops the theoretical knowledge and applied skills needed to understand empirical economic research and to plan and execute empirical projects. Topics …
Data Science For Economists - hse.ru
The objective of this course is to provide students with a hands on introduction to data science in economics (or more broadly to data science in the social sciences). The course consists of …
Data science in economics and finance: Introduction
Data science is a thriving, broad discipline that combines the best of classical and modern statistics, as well as applied and theoretical econometrics, machine learning, probability, and …
Data Science: A Primer for Economists - LMU
Data Science is the set of steps necessary–from the design of data collection to the preparation of analytical and visual content–for the provision of actionable insights to stakeholders.
Economics 300 Data Science for Economists, Spring 2023
Material for economists: 1. Quantitative Economics by Thomas J. Sargent and John Stachurski. 2. Python for Economists by Alex Bell.
Insight Report Data Science in the New Economy - World …
Jul 1, 2019 · his new source of value in the global economy. This Report focuses on data science, among the most competitive skills of the Fourth Industrial Revolution, in collaboration with …
ECON 3880 - An Introduction to Economic Data Science
Welcome to Economic Data Science! This course introduces you to fundamental skills and practical training necessary to analyze complex data and carry out research using data …
Data Science for Economics and Finance - Università Bocconi
Abstract The big data revolution and recent advancements in computing power have increased the interest in credit scoring techniques based on artificial intelli- gence.
Data Science for Economists
One of the key practices of clean code is to abstract away complexity. This is what packages do. They abstract away the complexity to make code easier to read, write, and debug. They offer a …
ECON 520: Data Science for Economists - Pedro H. C. Sant'Anna
•So far, in this course we have spent a lot of time dedicated to the “Data Science” pipeline. •Focus has been on tools: •Github for version control •Writing clean code •Web scrapping •Data …
Data Science Technologies in Economics and Finance: A …
Data science technologies allow economists to deal with all these issues. On the one hand, new big data sources can integrate and augment the information carried by publicly available …
MACHINE LEARNING & ANALYTICS FOR ECONOMISTS
To acquire an integrated approach to data as a strategic asset and to understand the real usefulness of data analytics techniques in a real world. To have a broad panorama of the …
Data Science for Economists
How we can combine the tools of economic theory, econometrics, and ML to build better empirical economic models? In what ways do econometrics and machine learning differ? What would …
Data Science for Economists
This lecture covers the bread-and-butter tool of applied econometrics and data science: regression analysis. This adapted from work by Grant McDermott. Today is focused on …
Data Science for Economists
Data type determines what method you can use to read and analyze the data A difference-in-difference model requires a different data shape than a regression discontinuity model
Data Science for Economists
Data Science for Economists Lecture 6a: Web Data in Research Kyle Coombs (he/him/his) Bates College | EC/DCS 368
Data Science for Economists
The tools that we're using all form part of a coherent data science ecosystem. Greatly reduces the cognitive overhead ("aggregation") associated with traditional workows, where you have to …
Data science for economists (EC 607) - GitHub
We will cover topics like version control and effective project management; programming; data acquisition (e.g. web-scraping), cleaning and visualization; GIS and remote sensing products; …
EC349-15 Data Science for Economists
The module will introduce students to the meaning of data science, working practically with data in R. Students will learn how to source, manipulate and analyse large data flows, extract …
Data Science for Economists
Fill in the gaps left by traditional econometrics and methods classes. Practical skills that tools that will bene t your thesis and future career. Neglected skills like how to actually nd datasets in the …
Econometric DATA SCIENCE - Massachusetts Institute of …
Econometric Data Science develops the theoretical knowledge and applied skills needed to understand empirical economic research and to plan and execute empirical projects. Topics …
Data Science For Economists - hse.ru
The objective of this course is to provide students with a hands on introduction to data science in economics (or more broadly to data science in the social sciences). The course consists of …
Data science in economics and finance: Introduction
Data science is a thriving, broad discipline that combines the best of classical and modern statistics, as well as applied and theoretical econometrics, machine learning, probability, and …
Data Science: A Primer for Economists - LMU
Data Science is the set of steps necessary–from the design of data collection to the preparation of analytical and visual content–for the provision of actionable insights to stakeholders.
Economics 300 Data Science for Economists, Spring 2023
Material for economists: 1. Quantitative Economics by Thomas J. Sargent and John Stachurski. 2. Python for Economists by Alex Bell.
Insight Report Data Science in the New Economy - World …
Jul 1, 2019 · his new source of value in the global economy. This Report focuses on data science, among the most competitive skills of the Fourth Industrial Revolution, in collaboration with …
ECON 3880 - An Introduction to Economic Data Science
Welcome to Economic Data Science! This course introduces you to fundamental skills and practical training necessary to analyze complex data and carry out research using data …
Data Science for Economics and Finance - Università Bocconi
Abstract The big data revolution and recent advancements in computing power have increased the interest in credit scoring techniques based on artificial intelli- gence.
Data Science for Economists
One of the key practices of clean code is to abstract away complexity. This is what packages do. They abstract away the complexity to make code easier to read, write, and debug. They offer a …
ECON 520: Data Science for Economists - Pedro H. C.
•So far, in this course we have spent a lot of time dedicated to the “Data Science” pipeline. •Focus has been on tools: •Github for version control •Writing clean code •Web scrapping •Data …
Data Science Technologies in Economics and Finance: A …
Data science technologies allow economists to deal with all these issues. On the one hand, new big data sources can integrate and augment the information carried by publicly available …
MACHINE LEARNING & ANALYTICS FOR ECONOMISTS
To acquire an integrated approach to data as a strategic asset and to understand the real usefulness of data analytics techniques in a real world. To have a broad panorama of the …
Data Science for Economists
How we can combine the tools of economic theory, econometrics, and ML to build better empirical economic models? In what ways do econometrics and machine learning differ? What would …
Data Science for Economists
This lecture covers the bread-and-butter tool of applied econometrics and data science: regression analysis. This adapted from work by Grant McDermott. Today is focused on …
Data Science for Economists
Data type determines what method you can use to read and analyze the data A difference-in-difference model requires a different data shape than a regression discontinuity model
Data Science for Economists
Data Science for Economists Lecture 6a: Web Data in Research Kyle Coombs (he/him/his) Bates College | EC/DCS 368
Data Science for Economists
The tools that we're using all form part of a coherent data science ecosystem. Greatly reduces the cognitive overhead ("aggregation") associated with traditional workows, where you have to …
