An accessible and fun guide to the essential tools of econometric research\nApplied 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.\nThe 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.\nWielding econometric tools with skill and confidence, Mastering 'Metrics uses data and statistics to illuminate the path from cause to effect.\n\nShows why econometrics is important\nExplains econometric research through humorous and accessible discussion\nOutlines empirical methods central to modern econometric practice\nWorks through interesting and relevant real-world examples\n

Discover how empirical researchers today actually think about and apply econometric methods with the practical, professional approach in Wooldridge's INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 5E. Unlike traditional books on the subject, INTRODUCTORY ECONOMETRICS' unique presentation demonstrates how econometrics has moved beyond just a set of abstract tools to become a genuinely useful tool for answering questions in business, policy evaluation, and forecasting environments. Organized around the type of data being analyzed, the book uses a systematic approach that only introduces assumptions as they are needed, which makes the material easier to understand and ultimately leads to better econometric practices. Packed with timely, relevant applications, the text emphasizes incorporates close to 100 intriguing data sets in six formats and offers updates that reflect the latest emerging developments in the field.

For courses in introductory econometrics. This package includes MyLab Economics. Engaging applications bring the theory and practice of modern econometrics to life Ensure students grasp the relevance of econometrics with Introduction to Econometrics -- the text that connects modern theory and practice with motivating, engaging applications. The 4th Edition maintains a focus on currency, while building on the philosophy that applications should drive the theory, not the other way around. The text incorporates real-world questions and data, and methods that are immediately relevant to the applications. With very large data sets increasingly being used in economics and related fields, a new chapter dedicated to Big Data helps students learn about this growing and exciting area. This coverage and approach make the subject come alive for students and helps them to become sophisticated consumers of econometrics. Also available with MyLab Economics By combining trusted author content with digital tools and a flexible platform, MyLab™ personalizes the learning experience and improves results for each student. Note: You are purchasing a standalone product; MyLab Economics does not come packaged with this content. Students, if interested in purchasing this title with MyLab Economics, ask your instructor to confirm the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information. If you would like to purchase both the physical text and MyLab Economics, search for: 0134610989 / 9780134610986 Introduction to Econometrics Plus MyLab Economics with Pearson eText -- Access Card Package, 4/e Package consists of: \n 0134461991 / 9780134461991 Introduction to Econometrics 0134543939 / 9780134543932 MyLab Economics with Pearson eText -- Access Card -- for Introduction to Econometrics

The second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated.\nThe second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis.\nEconometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.

An outstanding introduction to microeconometrics and how to do microeconometric research using Stata, this book covers topics often left out of microeconometrics textbooks and omitted from basic introductions to Stata. Cameron and Trivedi provide the most complete and up-to-date survey of microeconometric methods available in Stata. They begin by introducing simulation methods and then use them to illustrate features of the estimators and tests described in the rest of the book. They address each topic with an in-depth Stata example and demonstrate how to use Stata’s programming features to implement methods for which Stata does not have a specific command.

For first-¿year graduate courses in Econometrics for Social Scientists. Bridging the gap between social science studies and econometric analysis Designed to bridge the gap between social science studies and field-econometrics, Econometric Analysis, 8th Edition presents this ever-growing area at an accessible level. The book first introduces readers to basic techniques, a rich variety of models, and underlying theory that is easy to put into practice. It then presents readers with a sufficient theoretical background to understand advanced techniques and to recognize new variants of established models. This focus, along with hundreds of worked numerical examples, ensures that readers can apply the theory to real-world application and are prepared to be successful economists in the field.

This is the perfect (and essential) supplement for all econometrics classes--from a rigorous first undergraduate course, to a first master's, to a PhD course.\n Explains what is going on in textbooks full of proofs and formulas\n Offers intuition, skepticism, insights, humor, and practical advice (dos and don’ts)\n Contains new chapters that cover instrumental variables and computational considerations\n Includes additional information on GMM, nonparametrics, and an introduction to wavelets \n

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.

For courses in Econometrics. A Clear, Practical Introduction to Econometrics Using Econometrics: A Practical Guide offers readers an innovative introduction to elementary econometrics. Through real-world examples and exercises, the book covers the topic of single-equation linear regression analysis in an easily understandable format. The Seventh Edition is appropriate for all levels: beginner econometric readers, regression users seeking a refresher, and experienced practitioners who want a convenient reference. Praised as one of the most important texts in the last 30 years, the book retains its clarity and practicality in previous editions with a number of substantial improvements throughout.

The latest groundbreaking tome from Tim Ferriss, the #1 New York Times best-selling author of The 4-Hour Workweek.From the author:“For the last two years, I’ve interviewed more than 200 world-class performers for my podcast, The Tim Ferriss Show. The guests range from super celebs (Jamie Foxx, Arnold Schwarzenegger, etc.) and athletes (icons of powerlifting, gymnastics, surfing, etc.) to legendary Special Operations commanders and black-market biochemists. For most of my guests, it’s the first time they’ve agreed to a two-to-three-hour interview. This unusual depth has helped make The Tim Ferriss Show the first business/interview podcast to pass 100 million downloads.“This book contains the distilled tools, tactics, and ‘inside baseball’ you won’t find anywhere else. It also includes new tips from past guests, and life lessons from new ‘guests’ you haven’t met.“What makes the show different is a relentless focus on actionable details. This is reflected in the questions. For example: What do these people do in the first sixty minutes of each morning? What do their workout routines look like, and why? What books have they gifted most to other people? What are the biggest wastes of time for novices in their field? What supplements do they take on a daily basis?“I don’t view myself as an interviewer. I view myself as an experimenter. If I can’t test something and replicate results in the messy reality of everyday life, I’m not interested.“Everything within these pages has been vetted, explored, and applied to my own life in some fashion. I’ve used dozens of the tactics and philosophies in high-stakes negotiations, high-risk environments, or large business dealings. The lessons have made me millions of dollars and saved me years of wasted effort and frustration.“I created this book, my ultimate notebook of high-leverage tools, for myself. It’s changed my life, and I hope the same for you.”

Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors―all leaders in the statistics community―introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition \nFour new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

#1 New York Times BestsellerLegendary venture capitalist John Doerr reveals how the goal-setting system of Objectives and Key Results (OKRs) has helped tech giants from Intel to Google achieve explosive growth—and how it can help any organization thrive.In the fall of 1999, John Doerr met with the founders of a start-up whom he'd just given $12.5 million, the biggest investment of his career. Larry Page and Sergey Brin had amazing technology, entrepreneurial energy, and sky-high ambitions, but no real business plan. For Google to change the world (or even to survive), Page and Brin had to learn how to make tough choices on priorities while keeping their team on track. They'd have to know when to pull the plug on losing propositions, to fail fast. And they needed timely, relevant data to track their progress—to measure what mattered.Doerr taught them about a proven approach to operating excellence: Objectives and Key Results. He had first discovered OKRs in the 1970s as an engineer at Intel, where the legendary Andy Grove ("the greatest manager of his or any era") drove the best-run company Doerr had ever seen. Later, as a venture capitalist, Doerr shared Grove's brainchild with more than fifty companies. Wherever the process was faithfully practiced, it worked.In this goal-setting system, objectives define what we seek to achieve; key results are how those top-priority goals will be attained with specific, measurable actions within a set time frame. Everyone's goals, from entry level to CEO, are transparent to the entire organization.The benefits are profound. OKRs surface an organization's most important work. They focus effort and foster coordination. They keep employees on track. They link objectives across silos to unify and strengthen the entire company. Along the way, OKRs enhance workplace satisfaction and boost retention.In Measure What Matters, Doerr shares a broad range of first-person, behind-the-scenes case studies, with narrators including Bono and Bill Gates, to demonstrate the focus, agility, and explosive growth that OKRs have spurred at so many great organizations. This book will help a new generation of leaders capture the same magic.

A Guide to Econometrics has established itself as the first-choice text for teachers and students throughout the world. It provides an overview of the subject and an intuitive feel for its concepts and techniques without the notation and technical detail often characteristic of econometrics textbooks. The fourth edition updates the contents and references thoughout, while retaining the basic structure and flavor of earlier editions. New material has been added on several topics, such as bootstrapping, count data, duration models, generalized method of moments, instrumental variable estimation, linear structural relations, Monte Carlo studies, neural nets, time series analysis, and VARs. A new appendix and a new type of exercise underline the importance of the sampling distribution concept.

Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics. Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers. Through a logical structure that introduces and builds upon key concepts in a gradual way and slowly acclimatizes students to using R and Stan software, the book covers: \nAn introduction to probability and Bayesian inference Understanding Bayes′ rule Nuts and bolts of Bayesian analytic methods Computational Bayes and real-world Bayesian analysis Regression analysis and hierarchical methods \nThis unique guide will help students develop the statistical confidence and skills to put the Bayesian formula into practice, from the basic concepts of statistical inference to complex applications of analyses.

Now in paperback, “a compelling, accessible, and provocative piece of work that forces us to question many of our assumptions” (Gillian Tett, author of Fool’s Gold).Quants, physicists working on Wall Street as quantitative analysts, have been widely blamed for triggering financial crises with their complex mathematical models. Their formulas were meant to allow Wall Street to prosper without risk. But in this penetrating insider’s look at the recent economic collapse, Emanuel Derman—former head quant at Goldman Sachs—explains the collision between mathematical modeling and economics and what makes financial models so dangerous. Though such models imitate the style of physics and employ the language of mathematics, theories in physics aim for a description of reality—but in finance, models can shoot only for a very limited approximation of reality. Derman uses his firsthand experience in financial theory and practice to explain the complicated tangles that have paralyzed the economy. Models.Behaving.Badly. exposes Wall Street’s love affair with models, and shows us why nobody will ever be able to write a model that can encapsulate human behavior.

An accessible, contemporary introduction to the methods for determining cause and effect in the social sciences   "Causation versus correlation has been the basis of arguments--economic and otherwise--since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It's rare that a book prompts readers to expand their outlook; this one did for me."--Marvin Young (Young MC) Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied--for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages.

The primary objective of the fourth edition of Essentials of Econometrics is to provide a user-friendly introduction to econometric theory and techniques. This text provides a simple and straightforward introduction to econometrics for the beginner. The book is designed to help students understand econometric techniques through extensive examples, careful explanations, and a wide variety of problem material. In each of the editions, I have tried to incorporate major developments in the field in an intuitive and informative way without resort to matrix algebra, calculus, or statistics beyond the introductory level. The fourth edition continues that tradition.

Principles of Econometrics is an introductory book for undergraduate students in economics and finance, and can be used for MBA and first-year graduate students in many fields. The 4th Edition provides students with an understanding of why econometrics is necessary and a working knowledge of basic econometric tools. This text emphasizes motivation, understanding and implementation by introducing very simple economic models and asking economic questions that students can answer.

Gujarati and Porter's Basic Econometrics provides an elementary but comprehensive introduction to econometrics without resorting to matrix algebra, calculus, or statistics beyond the elementary level. With the addition of over 100 new data sets, as well as significantly updated research and examples, the Fifth Edition responds to important developments in the theory and practice of econometrics. Basic Econometrics is widely used by students of all fields as the expanded topics and concrete applications throughout the text apply to a broad range of studies.

This book is a supplement to Principles of Econometrics, 4th Edition by R. Carter Hill, William E. Griffiths and Guay C. Lim (Wiley, 2011). It is designed for students to learn the econometric software package EViews at the same time as they are using Principles of Econometrics to learn econometrics. It is not a substitute forPrinciples of Econometrics, nor is it a stand-alone computer manual. It is a companion to the textbook, showing how to do all the examples inPrinciples of Econometrics using EViews Version 7. For most students, econometrics only has real meaning after they are able to use it to analyze data sets, interpret results, and draw conclusions. EViews is an ideal vehicle for achieving these objectives. Others who wish to learn and practice econometrics, such as instructors and researchers, will also benefit from using this book in conjunction withPrinciples of Econometrics, 4th Edition.

Score your highest in econometrics? Easy. Econometrics can prove challenging for many students unfamiliar with the terms and concepts discussed in a typical econometrics course. Econometrics For Dummies eliminates that confusion with easy-to-understand explanations of important topics in the study of economics. Econometrics For Dummies breaks down this complex subject and provides you with an easy-to-follow course supplement to further refine your understanding of how econometrics works and how it can be applied in real-world situations. \nAn excellent resource for anyone participating in a college or graduate level econometrics course Provides you with an easy-to-follow introduction to the techniques and applications of econometrics Helps you score high on exam day \nIf you're seeking a degree in economics and looking for a plain-English guide to this often-intimidating course, Econometrics For Dummies has you covered.

Using Stata for Principles of Econometrics is a cutting edge text which incorporates the capabilities of Stata software to practically apply the principles of econometrics. Readers will learn how to apply basic econometric tools and the Stata software to estimation, inference and forecasting in the context of real world economic problems. In order to make concepts more accessible, it also offers lucid descriptions of techniques as well as appropriate applications to today's situations. Along the way, readers will find introductions to simple economic models and questions to enhance critical thinking.

A thorough and beginner-friendly introduction to econometrics. Using Econometrics: A Practical Guide provides readers with a practical introduction that combines single-equation linear regression analysis with real-world examples and exercises. This text also avoids complex matrix algebra and calculus, making it an ideal text for beginners. New problem sets and added support make Using Econometrics modern and easier to use.

First course in Econometrics in Economics Departments at better schools, also Economic/Business Forecasting. Statistics prerequisite but no calculus. Slightly higher level and more comprehensive than Gujarati (M-H, 1996) . P-R covers more time series and forecasting. P-R coverage is notch below Johnston-DiNardo (M-H, 97) and requires no matrix algebra. Includes data disk.

Every major econometric method is illustrated by a persuasive, real life example applied to real data. * Explores subjects such as sample design, which are critical to practical application econometrics.

The second edition of this bestselling textbook retains its unique learning-by-doing approach to econometrics. Rather than relying on complex theoretical discussions and complicated mathematics, this book explains econometrics from a practical point of view by walking the student through real-life examples, step by step. Damodar Gujarati’s clear, concise, writing style guides students from model formulation, to estimation and hypothesis-testing, through to post-estimation diagnostics. The basic statistics needed to follow the book are covered in an appendix, making the book a flexible and self-contained learning resource. The textbook is ideal for undergraduate students in economics, business, marketing, finance, operations research and related disciplines. It is also intended for students in MBA programs across the social sciences, and for researchers in business, government and research organizations who require econometrics.

This graduate text provides an intuitive but rigorous treatment of contemporary methods used in microeconometric research. The book makes clear that applied microeconometrics is about the estimation of marginal and treatment effects, and that parametric estimation is simply a means to this end. It also clarifies the distinction between causality and statistical association. The book focuses specifically on cross section and panel data methods. Population assumptions are stated separately from sampling assumptions, leading to simple statements as well as to important insights. The unified approach to linear and nonlinear models and to cross section and panel data enables straightforward coverage of more advanced methods. The numerous end-of-chapter problems are an important component of the book. Some problems contain important points not fully described in the text, and others cover new ideas that can be analyzed using tools presented in the current and previous chapters. Several problems require the use of the data sets located at the author's website.

Econometric Analysisi, 6/e serves as a bridge between an introduction to the field of econometrics and the professional literature for social scientists and other professionals in the field of social sciences, focusing on applied econometrics and theoretical background. This book provides a broad survey of the field of econometrics that allows the reader to move from here to practice in one or more specialized areas. At the same time, the reader will gain an appreciation of the common foundation of all the fields presented and use the tools they employ. This book gives space to a wide range of topics including basic econometrics, Classical, Bayesian, GMM, and Maximum likelihood, and gives special emphasis to new topics such a time series and panels. For social scientists and other professionals in the field who want a thorough introduction to applied econometrics that will prepare them for advanced study and practice in the field.

Hayashi's Econometrics promises to be the next great synthesis of modern econometrics. It introduces first year Ph.D. students to standard graduate econometrics material from a modern perspective. It covers all the standard material necessary for understanding the principal techniques of econometrics from ordinary least squares through cointegration. The book is also distinctive in developing both time-series and cross-section analysis fully, giving the reader a unified framework for understanding and integrating results.\n Econometrics has many useful features and covers all the important topics in econometrics 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 manner. Eight of the ten chapters include a serious empirical application drawn from labor economics, industrial organization, domestic and international finance, and macroeconomics. These empirical exercises at the end of each chapter provide students a hands-on experience applying the techniques covered in the chapter. The exposition is rigorous yet accessible to students who have 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.\n For those who intend to write a thesis on applied topics, the empirical applications of the book are a good way to learn how to conduct empirical research. For the theoretically inclined, the no-compromise treatment of the basic techniques is a good preparation for more advanced theory courses.

This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets.

This book provides an introduction to the field of microeconometrics through the use of R. The focus is on applying current learning from the field to real world problems. It uses R to both teach the concepts of the field and show the reader how the techniques can be used. It is aimed at the general reader with the equivalent of a bachelor’s degree in economics, statistics or some more technical field. It covers the standard tools of microeconometrics, OLS, instrumental variables, Heckman selection and difference in difference. In addition, it introduces bounds, factor models, mixture models and empirical Bayesian analysis.\nKey Features: Focuses on the assumptions underlying the algorithms rather than their statistical properties. Presents cutting-edge analysis of factor models and finite mixture models. Uses a hands-on approach to examine the assumptions made by the models and when the models fail to estimate accurately. Utilizes interesting real-world data sets that can be used to analyze important microeconomic problems. Introduces R programming concepts throughout the book. Includes appendices that discuss some of the standard statistical concepts and R programming used in the book.

Myoung-jae Lee reviews the three most popular methods (and their extensions) in applied economics and other social sciences: matching, regression discontinuity, and difference in differences. This book introduces the underlying econometric and statistical ideas, shows what is identified and how the identified parameters are estimated, and illustrates how they are applied with real empirical examples. Lee emphasizes how to implement the three methods with data: data and programs are provided in a useful online appendix. All readers-theoretical econometricians/statisticians, applied economists/social-scientists and researchers/students-will find something useful in the book from different perspectives.

This text prepares first-year graduate students and advanced undergraduates for empirical research in economics, and also equips them for specialization in econometric theory, business, and sociology.\nA Course in Econometrics is likely to be the text most thoroughly attuned to the needs of your students. Derived from the course taught by Arthur S. Goldberger at the University of Wisconsin–Madison and at Stanford University, it is specifically designed for use over two semesters, offers students the most thorough grounding in introductory statistical inference, and offers a substantial amount of interpretive material. The text brims with insights, strikes a balance between rigor and intuition, and provokes students to form their own critical opinions.\nA Course in Econometrics thoroughly covers the fundamentals―classical regression and simultaneous equations―and offers clear and logical explorations of asymptotic theory and nonlinear regression. To accommodate students with various levels of preparation, the text opens with a thorough review of statistical concepts and methods, then proceeds to the regression model and its variants. Bold subheadings introduce and highlight key concepts throughout each chapter.\nEach chapter concludes with a set of exercises specifically designed to reinforce and extend the material covered. Many of the exercises include real microdata analyses, and all are ideally suited to use as homework and test questions.

In An Introduction to Classical Econometric Theory Paul A. Ruud shows the practical value of an intuitive approach to econometrics. Students learn not only why but how things work. Through geometry, seemingly distinct ideas are presented as the result of one common principle, making econometrics more than mere recipes or special tricks. In doing this, the author relies on such concepts as the linear vector space, orthogonality, and distance. Parts I and II introduce the ordinary least squares fitting method and the classical linear regression model, separately rather than simultaneously as in other texts. Part III contains generalizations of the classical linear regression model and Part IV develops the latent variable models that distinguish econometrics from statistics. To motivate formal results in a chapter, the author begins with substantive empirical examples. Main results are followed by illustrative special cases; technical proofs appear toward the end of each chapter. Intended for a graduate audience, An Introduction to Classical Econometric Theory fills the gap between introductory and more advanced texts. It is the most conceptually complete text for graduate econometrics courses and will play a vital role in graduate instruction.

In this new and expanding area, Tony Lancaster’s text is the first comprehensive introduction to the Bayesian way of doing applied economics. \n Uses clear explanations and practical illustrations and problems to present innovative, computer-intensive ways for applied economists to use the Bayesian method; Emphasizes computation and the study of probability distributions by computer sampling;\n Covers all the standard econometric models, including linear and non-linear regression using cross-sectional, time series, and panel data;\n Details causal inference and inference about structural econometric models;\n Includes numerical and graphical examples in each chapter, demonstrating their solutions using the S programming language and Bugs software\n Supported by online supplements, including Data Sets and Solutions to Problems, at www.blackwellpublishing.com/lancaster

Tools to improve decision making in an imperfect world This publication provides readers with a thorough understanding of Bayesian analysis that is grounded in the theory of inference and optimal decision making. Contemporary Bayesian Econometrics and Statistics provides readers with state-of-the-art simulation methods and models that are used to solve complex real-world problems. Armed with a strong foundation in both theory and practical problem-solving tools, readers discover how to optimize decision making when faced with problems that involve limited or imperfect data. The book begins by examining the theoretical and mathematical foundations of Bayesian statistics to help readers understand how and why it is used in problem solving. The author then describes how modern simulation methods make Bayesian approaches practical using widely available mathematical applications software. In addition, the author details how models can be applied to specific problems, including: * Linear models and policy choices * Modeling with latent variables and missing data * Time series models and prediction * Comparison and evaluation of models The publication has been developed and fine- tuned through a decade of classroom experience, and readers will find the author's approach very engaging and accessible. There are nearly 200 examples and exercises to help readers see how effective use of Bayesian statistics enables them to make optimal decisions. MATLAB? and R computer programs are integrated throughout the book. An accompanying Web site provides readers with computer code for many examples and datasets. This publication is tailored for research professionals who use econometrics and similar statistical methods in their work. With its emphasis on practical problem solving and extensive use of examples and exercises, this is also an excellent textbook for graduate-level students in a broad range of fields, including economics, statistics, the social sciences, business, and public policy.

Bayesian Econometrics introduces the reader to the use of Bayesian methods in the field of econometrics at the advanced undergraduate or graduate level. The book is self-contained and does not require that readers have previous training in econometrics. The focus is on models used by applied economists and the computational techniques necessary to implement Bayesian methods when doing empirical work. The book includes numerous empirical examples and the website associated with it contains data sets and computer programs to help the student develop the computational skills of modern Bayesian econometrics.

Bayesian Econometric Methods examines principles of Bayesian inference by posing a series of theoretical and applied questions and providing detailed solutions to those questions. This second edition adds extensive coverage of models popular in finance and macroeconomics, including state space and unobserved components models, stochastic volatility models, ARCH, GARCH, and vector autoregressive models. The authors have also added many new exercises related to Gibbs sampling and Markov Chain Monte Carlo (MCMC) methods. The text includes regression-based and hierarchical specifications, models based upon latent variable representations, and mixture and time series specifications. MCMC methods are discussed and illustrated in detail - from introductory applications to those at the current research frontier - and MATLAB® computer programs are provided on the website accompanying the text. Suitable for graduate study in economics, the text should also be of interest to students studying statistics, finance, marketing, and agricultural economics.

Econometric models are widely used in the creation and evaluation of economic policy in the public and private sectors. But these models are useful only if they adequately account for the phenomena in question, and they can be quite misleading if they do not. In response, econometricians have developed tests and other checks for model adequacy. All of these methods, however, take as given the specification of the model to be tested. In this book, John Geweke addresses the critical earlier stage of model development, the point at which potential models are inherently incomplete.\n Summarizing and extending recent advances in Bayesian econometrics, Geweke shows how simple modern simulation methods can complement the creative process of model formulation. These methods, which are accessible to economics PhD students as well as to practicing applied econometricians, streamline the processes of model development and specification checking. Complete with illustrations from a wide variety of applications, this is an important contribution to econometrics that will interest economists and PhD students alike.

This concise textbook is an introduction to econometrics at the graduate or advanced undergraduate level. It differs from other books in econometrics in its use of the Bayesian approach to statistics. This approach, in contrast to the frequentist approach to statistics, makes explicit use of prior information and is based on the subjective view of probability, which takes probability theory as applying to all situations in which uncertainty exists, including uncertainty over the values of parameters.

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.

Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.

This book gives a broad and up-to-date coverage of bootstrap methods, with numerous applied examples, developed in a coherent way with the necessary theoretical basis. Applications include stratified data; finite populations; censored and missing data; linear, nonlinear, and smooth regression models; classification; time series and spatial problems. Special features of the book include: extensive discussion of significance tests and confidence intervals; material on various diagnostic methods; and methods for efficient computation, including improved Monte Carlo simulation. Each chapter includes both practical and theoretical exercises. Included with the book is a disk of purpose-written S-Plus programs for implementing the methods described in the text. Computer algorithms are clearly described, and computer code is included on a 3-inch, 1.4M disk for use with IBM computers and compatible machines. Users must have the S-Plus computer application. Author resource page: http://statwww.epfl.ch/davison/BMA/

A comprehensive introduction to bootstrap methods in the R programming environmentBootstrap methods provide a powerful approach to statistical data analysis, as they have more general applications than standard parametric methods. An Introduction to Bootstrap Methods with Applications to R explores the practicality of this approach and successfully utilizes R to illustrate applications for the bootstrap and other resampling methods. This book provides a modern introduction to bootstrap methods for readers who do not have an extensive background in advanced mathematics. Emphasis throughout is on the use of bootstrap methods as an exploratory tool, including its value in variable selection and other modeling environments. The authors begin with a description of bootstrap methods and its relationship to other resampling methods, along with an overview of the wide variety of applications of the approach. Subsequent chapters offer coverage of improved confidence set estimation, estimation of error rates in discriminant analysis, and applications to a wide variety of hypothesis testing and estimation problems, including pharmaceutical, genomics, and economics. To inform readers on the limitations of the method, the book also exhibits counterexamples to the consistency of bootstrap methods. An introduction to R programming provides the needed preparation to work with the numerous exercises and applications presented throughout the book. A related website houses the book's R subroutines, and an extensive listing of references provides resources for further study. Discussing the topic at a remarkably practical and accessible level, An Introduction to Bootstrap Methods with Applications to R is an excellent book for introductory courses on bootstrap and resampling methods at the upper-undergraduate and graduate levels. It also serves as an insightful reference for practitioners working with data in engineering, medicine, and the social sciences who would like to acquire a basic understanding of bootstrap methods.

APPLIED REGRESSION ANALYSIS applies regression to real data and examples while employing commercial statistical and spreadsheet software. Covering the core regression topics as well as optional topics including ANOVA, Time Series Forecasting, and Discriminant Analysis, the text emphasizes the importance of understanding the assumptions of the regression model, knowing how to validate a selected model for these assumptions, knowing when and how regression might be useful in a business setting, and understanding and interpreting output from statistical packages and spreadsheets.

The revised, eleventh edition of STATISTICS FOR BUSINESS AND ECONOMICS brings together more than twenty-five years of author experience, sound statistical methodology, a proven problem-scenario approach, and meaningful applications to demonstrate how statistical information informs decisions in the business world. And, to give you the most relevant material, you select the topics you want, including coverage of popular commercial statistical software programs like Minitab 16, Excel 2010, and others. These optional chapter appendices, coordinating data sets (on CD and online), and other support materials make STATISTICS FOR BUSINESS AND ECONOMICS the most customizable, efficient, and powerful approach to learning business statistics around.

Students in both social and natural sciences often seek regression methods to explain the frequency of events, such as visits to a doctor, auto accidents, or new patents awarded. This book provides the most comprehensive and up-to-date account of models and methods to interpret such data. The authors have conducted research in the field for more than twenty-five years. In this book, they combine theory and practice to make sophisticated methods of analysis accessible to researchers and practitioners working with widely different types of data and software in areas such as applied statistics, econometrics, marketing, operations research, actuarial studies, demography, biostatistics, and quantitative social sciences. The book may be used as a reference work on count models or by students seeking an authoritative overview. Complementary material in the form of data sets, template programs, and bibliographic resources can be accessed on the Internet through the authors' homepages. This second edition is an expanded and updated version of the first, with new empirical examples and more than one hundred new references added. The new material includes new theoretical topics, an updated and expanded treatment of cross-section models, coverage of bootstrap-based and simulation-based inference, expanded treatment of time series, multivariate and panel data, expanded treatment of endogenous regressors, coverage of quantile count regression, and a new chapter on Bayesian methods.

This book presents statistical methods for analysis of the duration of events. The primary focus is on models for single-spell data, events in which individual agents are observed for a single duration. Some attention is also given to multiple-spell data. The first part of the book covers model specification, including both structural and reduced form models and models with and without neglected heterogeneity. The book next deals with likelihood based inference about such models, with sections on full and semiparametric specification. A final section treats graphical and numerical methods of specification testing. This is the first published exposition of current econometric methods for the study of duration data.

Structural Macroeconometrics provides a thorough overview and in-depth exploration of methodologies, models, and techniques used to analyze forces shaping national economies. In this thoroughly revised second edition, David DeJong and Chetan Dave emphasize time series econometrics and unite theoretical and empirical research, while taking into account important new advances in the field.\n The authors detail strategies for solving dynamic structural models and present the full range of methods for characterizing and evaluating empirical implications, including calibration exercises, method-of-moment procedures, and likelihood-based procedures, both classical and Bayesian. The authors look at recent strides that have been made to enhance numerical efficiency, consider the expanded applicability of dynamic factor models, and examine the use of alternative assumptions involving learning and rational inattention on the part of decision makers. The treatment of methodologies for obtaining nonlinear model representations has been expanded, and linear and nonlinear model representations are integrated throughout the text. The book offers a rich array of implementation algorithms, sample empirical applications, and supporting computer code.\n Structural Macroeconometrics is the ideal textbook for graduate students seeking an introduction to macroeconomics and econometrics, and for advanced students pursuing applied research in macroeconomics. The book's historical perspective, along with its broad presentation of alternative methodologies, makes it an indispensable resource for academics and professionals.

This book deals with a number of mathematical topics that are of great importance in the study of classical econometrics, including matrix algebra, solutions to systems of linear equations and random forcing function.

Monte Carlo Simulation for Econometricians presents the fundamentals of Monte Carlo simulation (MCS), pointing to opportunities not often utilized in current practice, especially with regards to designing their general setup, controlling their accuracy, recognizing their shortcomings, and presenting their results in a coherent way. The author explores the properties of classic econometric inference techniques by simulation. The first three chapters focus on the basic tools of MCS. After treating the basic tools of MCS, Chapter 4 examines the crucial elements of analyzing the properties of asymptotic test procedures by MCS. Chapter 5 examines more general aspects of MCS, such as its history, possibilities to increase its efficiency and effectiveness, and whether synthetic random exogenous variables should be kept fixed over all the experiments or be treated as genuinely random and thus redrawn every replication. The simulation techniques discussed in the first five chapters are often addressed as naive or classic Monte Carlo methods. However, simulation can also be used not just for assessing the qualities of inference techniques, but also directly for obtaining inference in practice from empirical data. Various advanced inference techniques have been developed which incorporate simulation techniques. An early example of this is Monte Carlo testing, which corresponds to the parametric bootstrap technique. Chapter 6 highlights such techniques and presents a few examples of (semi-)parametric bootstrap techniques. This chapter also demonstrates that the bootstrap is not an alternative to MCS but just another practical inference technique, which uses simulation to produce econometric inference. Each chapter includes exercises allowing the reader to immerse in performing and interpreting MCS studies. The material has been used extensively in courses for undergraduate and graduate students. The various chapters contain illustrations which throw light on what uses can be made from MCS to discover the finite sample properties of a broad range of alternative econometric methods with a focus on the rather basic models and techniques.

Until now, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. Nonparametric Econometrics fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers.\n Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data--nominal and ordinal--in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory.\n This book is tailored to the needs of applied econometricians and social scientists. Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types--continuous, nominal, and ordinal--within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables.\n Nonparametric Econometrics covers all the material necessary to understand and apply nonparametric methods for real-world problems.

This book systematically and thoroughly covers a vast literature on the nonparametric and semiparametric statistics and econometrics that has evolved over the past five decades. Within this framework, this is the first book to discuss the principles of the nonparametric approach to the topics covered in a first year graduate course in econometrics, e.g., regression function, heteroskedasticity, simultaneous equations models, logit-probit and censored models. Professors Pagan and Ullah provide intuitive explanations of difficult concepts, heuristic developments of theory, and empirical examples emphasizing the usefulness of modern nonparametric approach. The book should provide a new perspective on teaching and research in applied subjects in general and econometrics and statistics in particular.

The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignores the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians. It discusses in depth, and in terms that someone with only one year of graduate econometrics can understand, basic to advanced nonparametric methods. The analysis starts with density estimation and motivates the procedures through methods that should be familiar to the reader. It then moves on to kernel regression, estimation with discrete data, and advanced methods such as estimation with panel data and instrumental variables models. The book pays close attention to the issues that arise with programming, computing speed, and application. In each chapter, the methods discussed are applied to actual data, paying attention to presentation of results and potential pitfalls.

Panel data econometrics has evolved rapidly over the last decade. Micro and Macro panels are increasing in numbers and availability and methods to deal with these data are in high demand from practitioners. Written by one of the world's leading researchers and writers in the field, Econometric Analysis of Panel Data has become established as the leading textbook for postgraduate courses in panel data. This new edition has been fully revised and updated and includes: \nA new chapter entitled Spatial Panel Data\n New empirical applications New material on non-stationary panels. New empirical applications using Stata and EViews. Thoroughly updated References. Additional exercises in each chapter This is a definitive book written by one of the architects of modern, panel data econometrics. It provides both a practical introduction to the subject matter, as well as a thorough discussion of the underlying statistical principles without taxing the reader too greatly. Since its first publication in 1995, it has quickly become a standard accompanying text in advanced econometrics courses around the world, and a major reference for researchers doing empirical work with longitudinal data."Professor Kajal Lahiri, State University of New York, Albany, USA. "Econometric Analysis of Panel Data is a classic in its field, used by researchers and graduate students throughout the world. In this new edition, Professor Baltagi has incorporated extensive new material, reflecting recent advances in the panel data literature in areas such as dynamic (including non-stationary) and limited dependent variable panel data models. It is an invaluable read for anyone interested in panel data." Professor Gary Koop, University of Strathclyde, UK. "This book is the most comprehensive work available on panel data. It is written by one of the leading contributors to the field, and is notable for its encyclopaedic coverage and its clarity of exposition. It is useful to theorists and to people doing applied work using panel data. It is valuable as a text for a course in panel data, as a supplementary text for more general courses in econometrics, and as a reference."Professor Peter Schmidt, Michigan State University, USA.

This book is a companion to Baltagi’s (2008) leading graduate econometrics textbook on panel data entitled Econometric Analysis of Panel Data, 4th Edition. \nThe book guides the student of panel data econometrics by solving exercises in a logical and pedagogical manner, helping the reader understand, learn and apply panel data methods. It is also a helpful tool for those who like to learn by solving exercises and running software to replicate empirical studies. It works as a complementary study guide to Baltagi (2008) and also as a stand alone book that builds up the reader’s confidence in working out difficult exercises in panel data econometrics and applying these methods to empirical work. The exercises start by providing some background information on partitioned regressions and the Frisch-Waugh-Lovell theorem. Then it goes through the basic material on fixed and random effects models in a one-way and two-way error components models: basic estimation, test of hypotheses and prediction. This include maximum likelihood estimation, testing for poolability of the data, testing for the significance of individual and time effects, as well as Hausman's test for correlated effects. It also provides extensions of panel data techniques to serial correlation, spatial correlation, heteroskedasticity, seemingly unrelated regressions, simultaneous equations, dynamic panel models, incomplete panels, measurement error, count panels, rotating panels, limited dependent variables, and non-stationary panels.

This book provides a comprehensive, coherent, and intuitive review of panel data methodologies that are useful for empirical analysis. Substantially revised from the second edition, it includes two new chapters on modeling cross-sectionally dependent data and dynamic systems of equations. Some of the more complicated concepts have been further streamlined. Other new material includes correlated random coefficient models, pseudo-panels, duration and count data models, quantile analysis, and alternative approaches for controlling the impact of unobserved heterogeneity in nonlinear panel data models.

Panel data econometrics uses both time series and cross-sectional data sets that have repeated observations over time for the same individuals (individuals can be workers, households, firms, industries, regions, or countries). This book reviews the most important topics in the subject. The three parts, dealing with static models, dynamic models, and discrete choice and related models are organized around the themes of controlling for unobserved heterogeneity and modelling dynamic responses and error components.About the SeriesAdvanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and cointegration. In both hardback and affordable paperback, each volume explains the nature and applicability of a topic in greater depth than possible in introductory textbooks or single journal articles. Each definitive work is formatted to be as accessible and convenient for those who are not familiar with the detailed primary literature.

This book is concerned with recent developments in time series and panel data techniques for the analysis of macroeconomic and financial data. It provides a rigorous, nevertheless user-friendly, account of the time series techniques dealing with univariate and multivariate time series models, as well as panel data models.It is distinct from other time series texts in the sense that it also covers panel data models and attempts at a more coherent integration of time series, multivariate analysis, and panel data models. It builds on the author's extensive research in the areas of time series and panel data analysis and covers a wide variety of topics in one volume. Different parts of the book can be used as teaching material for a variety of courses in econometrics. It can also be used as reference manual.It begins with an overview of basic econometric and statistical techniques, and provides an account of stochastic processes, univariate and multivariate time series, tests for unit roots, cointegration, impulse response analysis, autoregressive conditional heteroskedasticity models, simultaneous equation models, vector autoregressions, causality, forecasting, multivariate volatility models, panel data models, aggregation and global vector autoregressive models (GVAR). The techniques are illustrated using Microfit 5 (Pesaran and Pesaran, 2009, OUP) with applications to real output, inflation, interest rates, exchange rates, and stock prices.

Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions. This monograph is the first comprehensive treatment of the subject, encompassing models that are linear and nonlinear, parametric and nonparametric. Roger Koenker has devoted more than 25 years of research to the topic. The methods in his analysis are illustrated with a variety of applications from economics, biology, ecology and finance and will target audiences in econometrics, statistics, and applied mathematics in addition to the disciplines cited above. Author resource page: http://www.econ.uiuc.edu/~roger/research/rq/rq.html Roger Koenker is the winner of the 2010 Emanuel and Carol Parzen Prize for Statistical Innovation, awarded by the the Department of Statistics at Texas A&M University.

The Econometric Analysis of Network Data serves as an entry point for advanced students, researchers and data scientists who are seeking to perform effective analyses of networks, especially inference problems. It introduces the key results and ideas in an accessible, yet rigorous way, confining formal proofs to extensively annotated appendices. While a multi-contributed reference, the work is tightly focused and disciplined, providing latitude for varied specialties in one authorial voice. Each of the six worked examples describes relevant computational tools and provides a number of illustrative examples that are supported by a companion site code repository.Answers both the ‘why’ and ‘how’ questions in network analysisDescribes multiple worked examples from the literature and beyond, allowing empirical researchers and data scientists to quickly access the ‘state-of-the-art’Supported by a companion site code repository that details simulations and representative empirical applications in Python, MATLAB and C++Includes 40+ diagrams of ‘networks in the wild’ that help visually summarize key points

Integrating a contemporary approach to econometrics with the powerful computational tools offered by Stata, An Introduction to Modern Econometrics Using Stata focuses on the role of method-of-moments estimators, hypothesis testing, and specification analysis and provides practical examples that show how the theories are applied to real data sets using Stata. As an expert in Stata, the author successfully guides readers from the basic elements of Stata to the core econometric topics. He first describes the fundamental components needed to effectively use Stata. The book then covers the multiple linear regression model, linear and nonlinear Wald tests, constrained least-squares estimation, Lagrange multiplier tests, and hypothesis testing of nonnested models. Subsequent chapters center on the consequences of failures of the linear regression model's assumptions. The book also examines indicator variables, interaction effects, weak instruments, underidentification, and generalized method-of-moments estimation. The final chapters introduce panel-data analysis and discrete- and limited-dependent variables and the two appendices discuss how to import data into Stata and Stata programming. Presenting many of the econometric theories used in modern empirical research, this introduction illustrates how to apply these concepts using Stata. The book serves both as a supplementary text for undergraduate and graduate students and as a clear guide for economists and financial analysts.

In this second edition of An Introduction to Stata Programming, the author introduces concepts by providing the background and importance for the topic, presents common uses and examples, then concludes with larger, more applied examples referred to as "cookbook recipes." This is a great reference for anyone who wants to learn Stata programming. For those learning, the author assumes familiarity with Stata and gradually introduces more advanced programming tools. For the more advanced Stata programmer, the book introduces Stata’s Mata programming language and optimization routines.

A complete and up-to-date survey of microeconometric methods available in Stata, Microeconometrics Using Stata, Revised Edition is an outstanding introduction to microeconometrics and how to execute microeconometric research using Stata. It covers topics left out of most microeconometrics textbooks and omitted from basic introductions to Stata. This revised edition has been updated to reflect the new features available in Stata 11 that are useful to microeconomists. Instead of using mfx and the user-written margeff commands, the authors employ the new margins command, emphasizing both marginal effects at the means and average marginal effects. They also replace the xi command with factor variables, which allow you to specify indicator variables and interaction effects. Along with several new examples, this edition presents the new gmm command for generalized method of moments and nonlinear instrumental-variables estimation. In addition, the chapter on maximum likelihood estimation incorporates enhancements made to ml in Stata 11. Throughout the book, the authors use simulation methods to illustrate features of the estimators and tests described and provide an in-depth Stata example for each topic discussed. They also show how to use Stata’s programming features to implement methods for which Stata does not have a specific command. The unique combination of topics, intuitive introductions to methods, and detailed illustrations of Stata examples make this book an invaluable, hands-on addition to the library of anyone who uses microeconometric methods.

Introduction to Time Series Using Stata, Revised Edition provides a step-by-step guide to essential time-series techniques–from the incredibly simple to the quite complex– and, at the same time, demonstrates how these techniques can be applied in the Stata statistical package. The emphasis is on an understanding of the intuition underlying theoretical innovations and an ability to apply them. Real-world examples illustrate the application of each concept as it is introduced, and care is taken to highlight the pitfalls, as well as the power, of each new tool. The Revised Edition has been updated for Stata 16.

An Introduction to Survival Analysis Using Stata, Third Edition provides the foundation to understand various approaches for analyzing time-to-event data. It is not only a tutorial for learning survival analysis but also a valuable reference for using Stata to analyze survival data. Although the book assumes knowledge of statistical principles, simple probability, and basic Stata, it takes a practical, rather than mathematical, approach to the subject. This updated third edition highlights new features of Stata 11, including competing-risks analysis and the treatment of missing values via multiple imputation. Other additions include new diagnostic measures after Cox regression, Stata’s new treatment of categorical variables and interactions, and a new syntax for obtaining prediction and diagnostics after Cox regression. After reading this book, you will understand the formulas and gain intuition about how various survival analysis estimators work and what information they exploit. You will also acquire deeper, more comprehensive knowledge of the syntax, features, and underpinnings of Stata’s survival analysis routines.

This book grew from a one-semester course offered for many years to a mixed audience of graduate and undergraduate students who have not had the luxury of taking a course in measure theory. The core of the book covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms. In addition there are numerous sections treating topics traditionally thought of as more advanced, such as coupling and the KMT strong approximation, option pricing via the equivalent martingale measure, and the isoperimetric inequality for Gaussian processes. The book is not just a presentation of mathematical theory, but is also a discussion of why that theory takes its current form. It will be a secure starting point for anyone who needs to invoke rigorous probabilistic arguments and understand what they mean.

PROBABILITY AND MEASUREThird EditionNow in its new third edition, Probability and Measure offers advanced students, scientists, and engineers an integrated introduction to measure theory and probability. Retaining the unique approach of the previous editions, this text interweaves material on probability and measure, so that probability problems generate an interest in measure theory and measure theory is then developed and applied to probability. Probability and Measure provides thorough coverage of probability, measure, integration, random variables and expected values, convergence of distributions, derivatives and conditional probability, and stochastic processes. The Third Edition features an improved treatment of Brownian motion and the replacement of queuing theory with ergodic theory.Like the previous editions, this new edition will be well received by students of mathematics, statistics, economics, and a wide variety of disciplines that require a solid understanding of probability theory.

Approximation Theorems of Mathematical Statistics This convenient paperback edition makes a seminal text in statistics accessible to a new generation of students and practitioners. Approximation Theorems of Mathematical Statistics covers a broad range of limit theorems useful in mathematical statistics, along with methods of proof and techniques of application. The manipulation of "probability" theorems to obtain "statistical" theorems is emphasized. Besides a knowledge of these basic statistical theorems, this lucid introduction to the subject imparts an appreciation of the instrumental role of probability theory. The book makes accessible to students and practicing professionals in statistics, general mathematics, operations research, and engineering the essentials of: * The tools and foundations that are basic to asymptotic theory in statistics * The asymptotics of statistics computed from a sample, including transformations of vectors of more basic statistics, with emphasis on asymptotic distribution theory and strong convergence * Important special classes of statistics, such as maximum likelihood estimates and other asymptotic efficient procedures; W. Hoeffding's U-statistics and R. von Mises's "differentiable statistical functions" * Statistics obtained as solutions of equations ("M-estimates"), linear functions of order statistics ("L-statistics"), and rank statistics ("R-statistics") * Use of influence curves * Approaches toward asymptotic relative efficiency of statistical test procedures

Here is a practical and mathematically rigorous introduction to the field of asymptotic statistics. In addition to most of the standard topics of an asymptotics course--likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures--the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, one of the book's unifying themes that mainly entails the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation.

Kosorok’s brilliant text provides a self-contained introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. This is an authoritative text that covers all the bases, and also a friendly and gradual introduction to the area. The book can be used as research reference and textbook.

This book explores weak convergence theory and empirical processes and their applications to many applications in statistics. Part one reviews stochastic convergence in its various forms. Part two offers the theory of empirical processes in a form accessible to statisticians and probabilists. Part three covers a range of topics demonstrating the applicability of the theory to key questions such as measures of goodness of fit and the bootstrap.

This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described.The author begins with basic characteristics of financial time series data before covering three main topics: Analysis and application of univariate financial time series The return series of multiple assets Bayesian inference in finance methodsKey features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets.The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.

A complete set of statistical tools for beginning financial analysts from a leading authority Written by one of the leading experts on the topic, An Introduction to Analysis of Financial Data with R explores basic concepts of visualization of financial data. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research. The author supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case studies to illustrate actual implementations of the discussed methods. The book begins with the basics of financial data, discussing their summary statistics and related visualization methods. Subsequent chapters explore basic time series analysis and simple econometric models for business, finance, and economics as well as related topics including: \nLinear time series analysis, with coverage of exponential smoothing for forecasting and methods for model comparison Different approaches to calculating asset volatility and various volatility models High-frequency financial data and simple models for price changes, trading intensity, and realized volatility Quantitative methods for risk management, including value at risk and conditional value at risk Econometric and statistical methods for risk assessment based on extreme value theory and quantile regression \nThroughout the book, the visual nature of the topic is showcased through graphical representations in R, and two detailed case studies demonstrate the relevance of statistics in finance. A related website features additional data sets and R scripts so readers can create their own simulations and test their comprehension of the presented techniques. An Introduction to Analysis of Financial Data with R is an excellent book for introductory courses on time series and business statistics at the upper-undergraduate and graduate level. The book is also an excellent resource for researchers and practitioners in the fields of business, finance, and economics who would like to enhance their understanding of financial data and today's financial markets.

Applied Econometric Time Series, 4th Edition demonstrates modern techniques for developing models capable of forecasting, interpreting, and testing hypotheses concerning economic data. In this text, Dr. Walter Enders commits to using a “learn-by-doing” approach to help readers master time-series analysis efficiently and effectively.

This bestselling and thoroughly classroom-tested textbook is a complete resource for finance students. A comprehensive and illustrated discussion of the most common empirical approaches in finance prepares students for using econometrics in practice, while detailed case studies help them understand how the techniques are used in relevant financial contexts. Worked examples from the latest version of the popular statistical software EViews guide students to implement their own models and interpret results. Learning outcomes, key concepts and end-of-chapter review questions (with full solutions online) highlight the main chapter takeaways and allow students to self-assess their understanding. Building on the successful data- and problem-driven approach of previous editions, this third edition has been updated with new data, extensive examples and additional introductory material on mathematics, making the book more accessible to students encountering econometrics for the first time. A companion website, with numerous student and instructor resources, completes the learning package.

The availability of financial data recorded on high-frequency level has inspired a research area which over the last decade emerged to a major area in econometrics and statistics. The growing popularity of high-frequency econometrics is driven by technological progress in trading systems and an increasing importance of intraday trading, liquidity risk, optimal order placement as well as high-frequency volatility. This book provides a state-of-the art overview on the major approaches in high-frequency econometrics, including univariate and multivariate autoregressive conditional mean approaches for different types of high-frequency variables, intensity-based approaches for financial point processes and dynamic factor models. It discusses implementation details, provides insights into properties of high-frequency data as well as institutional settings and presents applications to volatility and liquidity estimation, order book modelling and market microstructure analysis.