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 researchApplied 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 importantExplains econometric research through humorous and accessible discussionOutlines empirical methods central to modern econometric practiceWorks through interesting and relevant real-world examples Read more

The Effect: An Introduction to Research Design and Causality is about research design, specifically concerning research that uses observational data to make a causal inference. It is separated into two halves, each with different approaches to that subject. The first half goes through the concepts of causality, with very little in the way of estimation. It introduces the concept of identification thoroughly and clearly and discusses it as a process of trying to isolate variation that has a causal interpretation. Subjects include heavy emphasis on data-generating processes and causal diagrams.Concepts are demonstrated with a heavy emphasis on graphical intuition and the question of what we do to data. When we “add a control variable” what does that actually do?Key Features: • Extensive code examples in R, Stata, and Python• Chapters on overlooked topics in econometrics classes: heterogeneous treatment effects, simulation and power analysis, new cutting-edge methods, and uncomfortable ignored assumptions• An easy-to-read conversational tone• Up-to-date coverage of methods with fast-moving literatures like difference-in-differences Read more

From Joshua Angrist, winner of the Nobel Prize in Economics, and Jörn-Steffen Pischke, an irreverent guide to the essentials of econometricsThe core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes. In the modern experimentalist paradigm, these techniques address clear causal questions such as: Do smaller classes increase learning? Should wife batterers be arrested? How much does education raise wages? Mostly Harmless Econometrics shows how the basic tools of applied econometrics allow the data to speak.In addition to econometric essentials, Mostly Harmless Econometrics covers important new extensions―regression-discontinuity designs and quantile regression―as well as how to get standard errors right. Joshua Angrist and Jörn-Steffen Pischke explain why fancier econometric techniques are typically unnecessary and even dangerous. The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social science.An irreverent review of econometric essentialsA focus on tools that applied researchers use mostChapters on regression-discontinuity designs, quantile regression, and standard errorsMany empirical examplesA clear and concise resource with wide applications Read more

The most authoritative and up-to-date core econometrics textbook availableEconometrics is the quantitative language of economic theory, analysis, and empirical work, and it has become a cornerstone of graduate economics programs. Econometrics provides graduate and PhD students with an essential introduction to this foundational subject in economics and serves as an invaluable reference for researchers and practitioners. This comprehensive textbook teaches fundamental concepts, emphasizes modern, real-world applications, and gives students an intuitive understanding of econometrics.Covers the full breadth of econometric theory and methods with mathematical rigor while emphasizing intuitive explanations that are accessible to students of all backgroundsDraws on integrated, research-level datasets, provided on an accompanying websiteDiscusses linear econometrics, time series, panel data, nonparametric methods, nonlinear econometric models, and modern machine learningFeatures hundreds of exercises that enable students to learn by doingIncludes in-depth appendices on matrix algebra and useful inequalities and a wealth of real-world examplesCan serve as a core textbook for a first-year PhD course in econometrics and as a follow-up to Bruce E. Hansen’s Probability and Statistics for Economists Read more

This is an introductory textbook on spatial analysis and spatial statistics through GIS. Each chapter presents methods and metrics, explains how to interpret results, and provides worked examples. Topics include: describing and mapping data through exploratory spatial data analysis; analyzing geographic distributions and point patterns; spatial autocorrelation; spatial clustering; geographically weighted regression and OLS regression; and spatial econometrics. The worked examples link theory to practice through a single real-world case study, with software and illustrated guidance. Exercises are solved twice: first through ArcGIS, and then GeoDa. Through a simple methodological framework the book describes the dataset, explores spatial relations and associations, and builds models. Results are critically interpreted, and the advantages and pitfalls of using various spatial analysis methods are discussed. This is a valuable resource for graduate students and researchers analyzing geospatial data through a spatial analysis lens, including those using GIS in the environmental sciences, geography, and social sciences. Read more

A brand new, fully updated edition of a popular classic on matrix differential calculus with applications in statistics and econometricsThis exhaustive, self-contained book on matrix theory and matrix differential calculus provides a treatment of matrix calculus based on differentials and shows how easy it is to use this theory once you have mastered the technique. Jan Magnus, who, along with the late Heinz Neudecker, pioneered the theory, develops it further in this new edition and provides many examples along the way to support it.Matrix calculus has become an essential tool for quantitative methods in a large number of applications, ranging from social and behavioral sciences to econometrics. It is still relevant and used today in a wide range of subjects such as the biosciences and psychology. Matrix Differential Calculus with Applications in Statistics and Econometrics, Third Edition contains all of the essentials of multivariable calculus with an emphasis on the use of differentials. It starts by presenting a concise, yet thorough overview of matrix algebra, then goes on to develop the theory of differentials. The rest of the text combines the theory and application of matrix differential calculus, providing the practitioner and researcher with both a quick review and a detailed reference.Fulfills the need for an updated and unified treatment of matrix differential calculusContains many new examples and exercises based on questions asked of the author over the yearsCovers new developments in field and features new applicationsWritten by a leading expert and pioneer of the theoryPart of the Wiley Series in Probability and Statistics Matrix Differential Calculus With Applications in Statistics and Econometrics Third Edition is an ideal text for graduate students and academics studying the subject, as well as for postgraduates and specialists working in biosciences and psychology. Read more

This book provides the most comprehensive treatment of the theoretical concepts and modelling techniques of quantitative risk management. Whether you are a financial risk analyst, actuary, regulator or student of quantitative finance, Quantitative Risk Management gives you the practical tools you need to solve real-world problems.Describing the latest advances in the field, Quantitative Risk Management covers the methods for market, credit and operational risk modelling. It places standard industry approaches on a more formal footing and explores key concepts such as loss distributions, risk measures and risk aggregation and allocation principles. The book's methodology draws on diverse quantitative disciplines, from mathematical finance and statistics to econometrics and actuarial mathematics. A primary theme throughout is the need to satisfactorily address extreme outcomes and the dependence of key risk drivers. Proven in the classroom, the book also covers advanced topics like credit derivatives.Fully revised and expanded to reflect developments in the field since the financial crisisFeatures shorter chapters to facilitate teaching and learningProvides enhanced coverage of Solvency II and insurance risk management and extended treatment of credit risk, including counterparty credit risk and CDO pricingIncludes a new chapter on market risk and new material on risk measures and risk aggregation Read more

**WITH UPDATED CHARACTERS - I apologize for the inconvenience caused to my Kindle readers. Not knowing statistics can lead to a loss of money, time, and accurate information.What am I looking at? What do these numbers mean? Why? These are frequent thoughts of those who don’t know much about statistics.“I’m not a number’s person” is not a good excuse to avoid learning the basics of this essential skill. Are you a person who earns money? Do you shop at the supermarket? Do you vote? Do you read the news? I’m sure you do.Learn to make decisions like world leaders do.Do you like to make uninformed, often poor decisions? Are you okay with being manipulated by skewed charts and diagrams? How about being lied to about the effectiveness of a product? I’m sure you don’t.Statistics can help you make exponentially better calls on what to buy, who to listen to, and what to believe.This book offers a detailed, illustrated breakdown of the fundamentals of statistics. Develop and use formal logical thinking abilities to understand the message behind numbers and charts in science, politics, and economy.Sharpen your critical and analytic thinking skills.Know what to look for when analyzing data. Information gets skewed – often unintentionally – because of the mainstream ways of doing statistics that didn’t catch up to big data. Stop staying in the dark. This book shines the light on the most common statistical methods - and their most frequent misuse. This step-by-step guide not only helps you detect what goes wrong in statistics but also educates you on how to utilize invaluable information statistics gets right to your benefit.Avoid making decisions on misleading information.- How to Use Descriptive and Inferential Statistics to Understand the World.- Be Wary of Misleading Charts.- Make Better Decisions Using Probability.- Understand P-Values in Research.- Understand Potential Bias in Studies.Albert Rutherford is the internationally bestselling author of several books on systems thinking, game theory, and mathematical thinking. Jae H. Kim is a freelance writer in econometrics, statistics, and data analysis. Since obtaining his PhD in econometrics in 1997, he has been a professor in major Australian universities until 2022. He has published more than 70 academic articles and book chapters in econometrics, empirical finance, economics, and applied statistics, which have attracted nearly 5000 citations to date.Learn basic statistics and spend your money wisely.Statistics, as a learning tool, can be used or misused. Some will actively lie and mislead with statistics. More often, however, well-meaning people – even professionals - unintentionally report incorrect statistical conclusions. Knowing what errors and mistakes to look for will help you to be in a better position to evaluate the information you have been given. Read more

Bayesian econometric methods have enjoyed an increase in popularity in recent years. Econometricians, empirical economists, and policymakers are increasingly making use of Bayesian methods. This handbook is a single source for researchers and policymakers wanting to learn about Bayesian methods in specialized fields, and for graduate students seeking to make the final step from textbook learning to the research frontier. It contains contributions by leading Bayesians on the latest developments in their specific fields of expertise. The volume provides broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing. It reviews the state of the art in Bayesian econometric methodology, with chapters on posterior simulation and Markov chain Monte Carlo methods, Bayesian nonparametric techniques, and the specialized tools used by Bayesian time series econometricians such as state space models and particle filtering. It also includes chapters on Bayesian principles and methodology. Read more

An engaging and practical introduction to econometrics, Real Econometrics: The Right Tools to Answer Important Questions, offers thorough coverage of the most frequently used methods of analysis. Grounded in contemporary understandings of causal inference, the text invites students to extract meaningful information about important economic policy issues from available data. Bailey's emphasis on practical applications, combined with a lively and conversational narrative and a diverse array of examples and case studies, provides students with a solid foundation in the analytical tools they will use throughout their academic and professional careers. The second edition includes new conceptual exercises, revised appendices, and additional code and guidance for R software. Read more

Gain an understanding of how econometrics can answer today's questions in business, policy evaluation and forecasting with Wooldridge's INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 7E. Unlike traditional texts, this book's practical, yet professional, approach demonstrates how econometrics has moved beyond a set of abstract tools to become genuinely useful for answering questions across a variety of disciplines.The author has organized the book's presentation around the type of data being analyzed with a systematic approach that only introduces assumptions as they are needed. This makes the material easier to understand and, ultimately, leads to better econometric practices. Packed with relevant applications, the text incorporates more than 100 data sets in different formats. Updates introduce the latest developments in the field, including the recent advances in the so-called “causal effects” or “treatment effects,"" to provide a complete understanding of the impact and importance of econometrics today. Read more

This attractive textbook with its easy-to-follow presentation provides a down-to-earth introduction to operations research for students in a wide range of fields such as engineering, business analytics, mathematics and statistics, computer science, and econometrics. It is the result of many years of teaching and collective feedback from students.The book covers the basic models in both deterministic and stochastic operations research and is a springboard to more specialized texts, either practical or theoretical. The emphasis is on useful models and interpreting the solutions in the context of concrete applications.The text is divided into several parts. The first three chapters deal exclusively with deterministic models, including linear programming with sensitivity analysis, integer programming and heuristics, and network analysis. The next three chapters primarily cover basic stochastic models and techniques, including decision trees, dynamic programming, optimal stopping, production planning, and inventory control. The final five chapters contain more advanced material, such as discrete-time and continuous-time Markov chains, Markov decision processes, queueing models, and discrete-event simulation.Each chapter contains numerous exercises, and a large selection of exercises includes solutions. Read more

Unlock the potential of advanced mathematical tools in econometrics with this comprehensive guide on tensor networks and multilinear algebra. Discover how these cutting-edge techniques can transform econometric analysis, enabling you to handle complex, high-dimensional datasets with ease and precision.Key Features:In-Depth Exploration: Dive into the rich landscape of tensor methods and multilinear algebra tailored for econometrics.Practical Python Code: Implement the concepts with practical examples and code snippets provided in Python for each chapter, ensuring hands-on experience.Versatile Application: Learn how these methodologies enhance econometric models, data analysis, machine learning, and more.Book Description:This book serves as a definitive resource for econometricians, data scientists, and researchers eager to harness the power of tensor computations and multilinear algebra. It begins with fundamental concepts and progresses to advanced applications, all supported by Python code examples. Whether you're dealing with time series data, panel data, or high-dimensional datasets, you'll find innovative solutions and tools to streamline your econometric modeling processes.What You Will Learn:Master the Kronecker Product for constructing robust multilinear models.Simplify tensor data using Canonical Polyadic Decomposition for econometric analysis.Apply Tucker Decomposition to effectively reduce tensor dimensionality.Implement Higher-Order Singular Value Decomposition (HOSVD) for tensor applications.Explore PARAFAC for comprehensive multi-dimensional econometric factor analysis.Build and refine tensor regression models for high-dimensional data.Employ Alternating Least Squares (ALS) for efficient tensor decomposition computation.Understand Kruskal's Theorem to ensure identifiability in tensor models.Utilize Multilinear Principal Component Analysis to extract key data features.Apply tensor completion methods to handle missing econometric data sets.Leverage tensor networks to model time series data effectively.Extend eigenvalue decomposition techniques to tensor spaces for advanced applications.Analyze panel data with innovative tensor-based methods.Develop Tensor-based Vector Autoregression (VAR) models for complex time series.Use multilinear rank to streamline econometric model complexity.Design and implement tensor-based estimators for efficient modeling.Investigate conditional independence in econometric tensor models.Explore structural equation modeling with tensor techniques.Employ regularization techniques like Lasso and Ridge in tensor regression.Construct nonlinear econometric models using tensor computations.Develop parameter identification strategies in tensor-based models.Utilize optimization algorithms to boost tensor calculations.Incorporate Bayesian methods in tensor factorization for probabilistic modeling.Solve tensor-related problems with convex optimization principles.Optimize tensor learning with stochastic gradient descent methods.Tackle high-dimensional econometric challenges using tensor networks.Implement randomized algorithms for scalable tensor computations.Integrate tensor methods with machine learning in econometrics.Design efficient data structures for tensor computations.Apply hierarchical Tucker decomposition for large-scale data reduction.Conduct classification tasks with multilinear discriminant analysis in econometrics.Boost model efficiency with sparse tensor methods.Investigate quantum tensor networks for groundbreaking data analysis. Read more

In this book, there is a strong emphasis on application with the necessary mathematical grounding. There are plenty of worked examples with all solutions provided. This enlarged new edition includes generalised Fourier series and a completely new chapter on wavelets.Only knowledge of elementary trigonometry and calculus are required as prerequisites. An Introduction to Laplace Transforms and Fourier Series will be useful for second and third year undergraduate students in engineering, physics or mathematics, as well as for graduates in any discipline such as financial mathematics, econometrics and biological modelling requiring techniques for solving initial value problems. Read more