Preface 35 Part IV Part IV takes up regression withtime.Chapter 15 focuseson forecasting and introduces various modern tools for analyzing time series regressions,such as tests for stability.Chapter 16 discusses the use of time series data to estimate causal relations.Chapter 17 presents some more advanced tools for time series analysis including models of volatility clustering and dynamic factor models Part V Part V is an introduction to econometric theory.This part is more than an appendix that fills in mathematical details omitted from the text.Rather,it is a self-contained treatment of the econometric theory of estimation and inference in the linear regres sion model.Chapter 18 develops the theory of regression analysis for a single regres- sor:the exposition does not use matrix algebra.although it does demand a higher level of mathematical sophistication than the rest of the text.Chapter 19 presents the multiple regression model,instrumental variables regression,generalized method of moments estimation of the linear model,and principal components analysis,all in matrix form Prerequisites Within the Book Because different instructors like to emphasize different material,we wrote this book with diverse teaching preferences in mind.To the maximum extent possible the chapters in Parts III,IV.and V are "stand-alone in the sense that they do not require first teaching all the preceding chapters.The specific prerequisites for each chapter are described in Table I.Although we have found that the sequence of topics adopted in the text works well in our own courses,the chapters are writ- ten in a way that allows instructors to present topics in a different order if they so desire
Part IV Part IV takes up regression with time series data. Chapter 15 focuses on forecasting and introduces various modern tools for analyzing time series regressions, such as tests for stability. Chapter 16 discusses the use of time series data to estimate causal relations. Chapter 17 presents some more advanced tools for time series analysis, including models of volatility clustering and dynamic factor models. Part V Part V is an introduction to econometric theory. This part is more than an appendix that fills in mathematical details omitted from the text. Rather, it is a self-contained treatment of the econometric theory of estimation and inference in the linear regression model. Chapter 18 develops the theory of regression analysis for a single regressor; the exposition does not use matrix algebra, although it does demand a higher level of mathematical sophistication than the rest of the text. Chapter 19 presents the multiple regression model, instrumental variables regression, generalized method of moments estimation of the linear model, and principal components analysis, all in matrix form. Prerequisites Within the Book Because different instructors like to emphasize different material, we wrote this book with diverse teaching preferences in mind. To the maximum extent possible, the chapters in Parts III, IV, and V are “stand-alone” in the sense that they do not require first teaching all the preceding chapters. The specific prerequisites for each chapter are described in Table I. Although we have found that the sequence of topics adopted in the text works well in our own courses, the chapters are written in a way that allows instructors to present topics in a different order if they so desire. Preface 35 A01_STOC4455_04_GE_FM.indd 35 06/12/18 10:52 AM
Preface Sample Courses This book accommodates several different course structures. 3Guide to Prerequisites for Special-Topic Chapters in Parts Ill,IV,and V Prerequisite parts or chapters PartIll PartV 10.1.12.1 Chapter 1-3 4-7.9 8 102 12215.1-15.415.5-15.816 18 10 11 121,12.2 12.3-12.6 X 13 X X X X 14 Xa X X 15 b 16 Xa b 17 b 18 19 X This table shows the minimu quisites needed to the material in a given chapter.For example.estimation of a) ters is throueh 17 (the time series chapters)can be taueht without first teaching chapter (nonlinear regression functions)if the instructor pauses to explain the use of logarithmic transformations to approximate percentage changes. Standard Introductory Econometrics This course introduces econometrics(Chapter 1)and reviews probability and sta tistics as needed (Chapters 2 and 3).It then moves on to regression with a single regressor.multiple regression.the basics of functional form analysis.and the evalua- tion of regression studies (all Part I).The cours proceeds to cov regr ession with panel data(Chapter 10).regression with a limited dependent variable(Chapter 11). and instrumental variables regression(Chapter 12).as time permits.The course then
Sample Courses This book accommodates several different course structures. TABLE I Guide to Prerequisites for Special-Topic Chapters in Parts III, IV, and V Prerequisite parts or chapters Part I Part II Part III Part IV Part V 10.1, 12.1, Chapter 1–3 4–7, 9 8 10.2 12.2 15.1–15.4 15.5–15.8 16 18 10 Xa Xa X 11 Xa Xa X 12.1, 12.2 Xa Xa X 12.3–12.6 Xa Xa X X X 13 Xa Xa X X X 14 Xa Xa X 15 Xa Xa b 16 Xa Xa b X 17 Xa Xa b X X X 18 X X X 19 X X X X X This table shows the minimum prerequisites needed to cover the material in a given chapter. For example, estimation of dynamic causal effects with time series data (Chapter 16) first requires Part I (as needed, depending on student preparation, and except as noted in footnote a), Part II (except for Chapter 8; see footnote b), and Sections 15.1 through 15.4. a Chapters 10 through 17 use exclusively large-sample approximations to sampling distributions, so the optional Sections 3.6 (the Student t distribution for testing means) and 5.6 (the Student t distribution for testing regression coefficients) can be skipped. b Chapters 15 through 17 (the time series chapters) can be taught without first teaching Chapter 8 (nonlinear regression functions) if the instructor pauses to explain the use of logarithmic transformations to approximate percentage changes. Standard Introductory Econometrics This course introduces econometrics (Chapter 1) and reviews probability and statistics as needed (Chapters 2 and 3). It then moves on to regression with a single regressor, multiple regression, the basics of functional form analysis, and the evaluation of regression studies (all Part II). The course proceeds to cover regression with panel data (Chapter 10), regression with a limited dependent variable (Chapter 11), and instrumental variables regression (Chapter 12), as time permits. The course then 36 Preface A01_STOC4455_04_GE_FM.indd 36 06/12/18 10:52 AM
Preface 37 turns to experiments and quasi-experiments in chapter 13.topics that provide an opportunity to return to the questions of estimating causal effects raised at the begin ning of the semester and to recapitulate core regression methods.If there is time. the students can be introduced to big data and machine learning methods at the end (Chapter 14).Prerequisites:Algebra Il and introductory statistics Introductory Econometrics with Time Series and Forecasting Applications Like a standard introductory course,this course covers all of Part I(as needed) and Part II.Optionally,the course next provides a brief introduction to panel data (Sections 10.1 and 10.2)and takes up instrumental variables regression(Chapter 12.or just Sections 12.1 and 12.2).The course then proceeds to Chapter 14(predic- tion in large cross sectional data sets).It then turns to Part IV.covering forecasting (Chapter 15)and estimation of dynamic causal effects(Chapter 16).If time permits. the course can include some advanced topics in time series analysis such as vola- ility clustering(Section 175)and forecasting with many predictors(Section 176) Prerequisites:Algebra II and introductory statistics. Applied Time Series Analysis and Forecasting This book also can be used for a short course on applied time series and forecasting for which a course on regression analysis is a prerequisite.Some time is spent review ing the tools of basic regression analysis in Part II,depending on student preparation. The course then moves directly to time series forecasting(Chapter 15),estimation of dynamic causal effects (Chapter 16).and advanced topics in time series analysis (Chapter 17)inluding vector autoregress ions.If there is time,the course can cover prediction using large data sets(Chapter 14 and Section 176),An important compo- nent of this course is hands-on forecasting exercises.available as the end-of-chapter Empirical Exercises for Chapters 15 and 17 Prerequisites:Algebra I and basic intr ductory econometrics or the equivalent Introduction to Econometric Theory This book is also suitable for an advanced undergraduate course in which the stu- dents have a strong mathematical preparation or for a master's level course in econometrics.The course briefly reviews the theory of statistics and probability as necessary (Part I).The course introduces regression analysis using the nonmath- ematical,applications-based treatment of Part II.This introduction is followed by the theoretical development in Chapters 18 and 19(through Section 19.5).The course then takes up regression with a limited dependent variable(Chapter 11) and maximum likelihood estimation (Appendix 11.2).Next,the course optionally turns to instrumental variables regression and generalized method of moments (Chapter 12 and Section 19.7).time series methods(Chapter 15),the estimationof
turns to experiments and quasi-experiments in Chapter 13, topics that provide an opportunity to return to the questions of estimating causal effects raised at the beginning of the semester and to recapitulate core regression methods. If there is time, the students can be introduced to big data and machine learning methods at the end (Chapter 14). Prerequisites: Algebra II and introductory statistics. Introductory Econometrics with Time Series and Forecasting Applications Like a standard introductory course, this course covers all of Part I (as needed) and Part II. Optionally, the course next provides a brief introduction to panel data (Sections 10.1 and 10.2) and takes up instrumental variables regression (Chapter 12, or just Sections 12.1 and 12.2). The course then proceeds to Chapter 14 (prediction in large cross sectional data sets). It then turns to Part IV, covering forecasting (Chapter 15) and estimation of dynamic causal effects (Chapter 16). If time permits, the course can include some advanced topics in time series analysis such as volatility clustering (Section 17.5) and forecasting with many predictors (Section 17.6). Prerequisites: Algebra II and introductory statistics. Applied Time Series Analysis and Forecasting This book also can be used for a short course on applied time series and forecasting, for which a course on regression analysis is a prerequisite. Some time is spent reviewing the tools of basic regression analysis in Part II, depending on student preparation. The course then moves directly to time series forecasting (Chapter 15), estimation of dynamic causal effects (Chapter 16), and advanced topics in time series analysis (Chapter 17), including vector autoregressions. If there is time, the course can cover prediction using large data sets (Chapter 14 and Section 17.6), An important component of this course is hands-on forecasting exercises, available as the end-of-chapter Empirical Exercises for Chapters 15 and 17. Prerequisites: Algebra II and basic introductory econometrics or the equivalent. Introduction to Econometric Theory This book is also suitable for an advanced undergraduate course in which the students have a strong mathematical preparation or for a master’s level course in econometrics. The course briefly reviews the theory of statistics and probability as necessary (Part I). The course introduces regression analysis using the nonmathematical, applications-based treatment of Part II. This introduction is followed by the theoretical development in Chapters 18 and 19 (through Section 19.5). The course then takes up regression with a limited dependent variable (Chapter 11) and maximum likelihood estimation (Appendix 11.2). Next, the course optionally turns to instrumental variables regression and generalized method of moments (Chapter 12 and Section 19.7), time series methods (Chapter 15), the estimation of Preface 37 A01_STOC4455_04_GE_FM.indd 37 06/12/18 10:52 AM
Preface causal effects using time series data and generalized least squares(Chapter 16 and Section 19.6).and/or to machine learning methods(Chapter 14 and Appendix 19.7). Prerequisites:Calculus and introductory statistics.Chapter 18 assumes previous exposure to matrix algebra. Instructor Teaching Resources This program comes with the following teaching resources: Supplements available to instructors at Features of the Supplemen www.pearsonglobaleditions.com Solutions Manual Solutions to the end-of-chapter content Test Bank 1000 multiple-choic and ek Kel.Claremont .Type (Multiple-choice,essay.graphical) Computerized TestGen TestGen allows instructors to: Customize save and generate classroom test .Edit,add,or delete questions from the Test ，em Files eof tests and student results PowerPoints PowerPoints mee sibility standards fo taeatabitesFearesincddc,bu .Keyboard and Screen Reader access Alternative text for images Companion Website Iexl,an
causal effects using time series data and generalized least squares (Chapter 16 and Section 19.6), and/or to machine learning methods (Chapter 14 and Appendix 19.7). Prerequisites: Calculus and introductory statistics. Chapter 18 assumes previous exposure to matrix algebra. Instructor Teaching Resources This program comes with the following teaching resources: Supplements available to instructors at www.pearsonglobaleditions.com Features of the Supplement Solutions Manual Solutions to the end-of-chapter content. Test Bank Authored by Manfred Keil, Claremont McKenna College 1,000 multiple-choice questions, essays and longer questions, and mathematical and graphical problems with these annotations: • Type (Multiple-choice, essay, graphical) Computerized TestGen TestGen allows instructors to: • Customize, save, and generate classroom tests • Edit, add, or delete questions from the Test Item Files • Analyze test results • Organize a database of tests and student results. PowerPoints Slides include all the graphs, tables, and equations in the text. PowerPoints meet accessibility standards for students with disabilities. Features include, but not limited to: • Keyboard and Screen Reader access • Alternative text for images • High color contrast between background and foreground colors Companion Website The Companion Website provides a wide range of additional resources for students and faculty. These resources include more and more in depth empirical exercises, data sets for the empirical exercises, replication files for empirical results reported in the text, and EViews tutorials. 38 Preface A01_STOC4455_04_GE_FM.indd 38 06/12/18 10:52 AM
Preface 39 Acknowledgments A great many people contributed to the first edition of this book.Our biggest debts of ratitude are to our colleagues at Harvard and Princeton who used early draft of this book in their classrooms.At Harvard's Kennedy School of Government, Suzanne Cooper provided invaluable suggestions and detailed comments on mul- tiple drafts As a coteacher with one of the authors(Stock).she also helped vet much of the material in this book while it was being developed for a required course for master's students at the Kennedy School.We are also indebted to two other Kennedy School colleagues at the time,Alberto Abadie and Sue Dynarski,for their patient explanations of quasi-experiments and the field of program evaluation and for thei detailed comments on early drafts of the text.At Princeton,Eli Tamer taught from an early draft and also provided helpful comments on the penultimate draft of the book. We also owe much to many of our friends and colleagues in econometrics who spent time talking with us about the substance of this book and made so many helpful suggestions.Bruce Hansen(University of Wisconsin-Madison)and Bo Honore(Princeton)provided helpful feedback on very early outlines and pre iminary versions of the e material in Part II.Joshua Angrist (MIT)and Guido Imbens (University of California.Berkelev)provided thoughtful suggestions abou our treatment of materials on program evaluation.Our presentation of the material on time series has benefited from discussions with Yacine Ait-Sahalia(Princeton). Graham Elliott(University of California,San Diego).Andrew Harvey(Cambridge University).and Christopher Sims(Princeton).Finally,many people made helpful suggestions on parts of the manuscript close to their area of expertise:Don Andrews (Yale),John Bound(Univer sity of Michigan).Gregory Chow (Princeton)Thoma Downes (Tufts).David Drukker (StataCorp.)Jean Baldwin Grossman (Princeton) Eric Hanushek(Hoover Institution).James Heckman(University of Chicago).Han Hong(Princeton).Caroline Hoxby(Harvard),Alan Krueger(Princeton).Steven Levitt(University of Chicago).Richard Light(Harvard).David Neumark(Michigan State University),Joseph Newhouse(Harvard),Pierre Perron(Boston University) Kenneth warner (University of michigan)and richard Zeckhauser (Harvard). Many people were very generous in providing us with data.The California tes score data were constructed with the assistance of Les Axelrod of the Standards and Assessments Division,California Department of Education.We are grateful to Charlie DePascale,Student Assessment Services,Massachusetts Department of Education,for his help with aspects of the Massachusetts test score data set Christopher Ruhm (University of North Carolina.Greensboro)graciously provided us with his data set on drunk driving laws and traffic fatalities.The research depart ment at the Federal Reserve Bank of Boston deserves thanks for putting togethe its data on racial discrimination in mortgage lending:we particularly thank Geoffrey Tootell for providing us with the updated version of the data set we use in Chapter 9
Acknowledgments A great many people contributed to the first edition of this book. Our biggest debts of gratitude are to our colleagues at Harvard and Princeton who used early drafts of this book in their classrooms. At Harvard’s Kennedy School of Government, Suzanne Cooper provided invaluable suggestions and detailed comments on multiple drafts. As a coteacher with one of the authors (Stock), she also helped vet much of the material in this book while it was being developed for a required course for master’s students at the Kennedy School. We are also indebted to two other Kennedy School colleagues at the time, Alberto Abadie and Sue Dynarski, for their patient explanations of quasi-experiments and the field of program evaluation and for their detailed comments on early drafts of the text. At Princeton, Eli Tamer taught from an early draft and also provided helpful comments on the penultimate draft of the book. We also owe much to many of our friends and colleagues in econometrics who spent time talking with us about the substance of this book and who collectively made so many helpful suggestions. Bruce Hansen (University of Wisconsin–Madison) and Bo Honore (Princeton) provided helpful feedback on very early outlines and preliminary versions of the core material in Part II. Joshua Angrist (MIT) and Guido Imbens (University of California, Berkeley) provided thoughtful suggestions about our treatment of materials on program evaluation. Our presentation of the material on time series has benefited from discussions with Yacine Ait-Sahalia (Princeton), Graham Elliott (University of California, San Diego), Andrew Harvey (Cambridge University), and Christopher Sims (Princeton). Finally, many people made helpful suggestions on parts of the manuscript close to their area of expertise: Don Andrews (Yale), John Bound (University of Michigan), Gregory Chow (Princeton), Thomas Downes (Tufts), David Drukker (StataCorp.), Jean Baldwin Grossman (Princeton), Eric Hanushek (Hoover Institution), James Heckman (University of Chicago), Han Hong (Princeton), Caroline Hoxby (Harvard), Alan Krueger (Princeton), Steven Levitt (University of Chicago), Richard Light (Harvard), David Neumark (Michigan State University), Joseph Newhouse (Harvard), Pierre Perron (Boston University), Kenneth Warner (University of Michigan), and Richard Zeckhauser (Harvard). Many people were very generous in providing us with data. The California test score data were constructed with the assistance of Les Axelrod of the Standards and Assessments Division, California Department of Education. We are grateful to Charlie DePascale, Student Assessment Services, Massachusetts Department of Education, for his help with aspects of the Massachusetts test score data set. Christopher Ruhm (University of North Carolina, Greensboro) graciously provided us with his data set on drunk driving laws and traffic fatalities. The research department at the Federal Reserve Bank of Boston deserves thanks for putting together its data on racial discrimination in mortgage lending; we particularly thank Geoffrey Tootell for providing us with the updated version of the data set we use in Chapter 9 and Lynn Browne for explaining its policy context. We thank Jonathan Gruber (MIT) for sharing his data on cigarette sales, which we analyze in Chapter 12, and Preface 39 A01_STOC4455_04_GE_FM.indd 39 06/12/18 10:52 AM