Preface data,which are provided on the text's Companion Website(www.pearsonglobaleditions .com)and in MyLab Economics. Throughout the text,we have focused on helping students understand,retain and apply the essential ideas.Chapter introductions provide real-world grounding and motivation,as well as brief road maps highlighting the sequence of the discus- sion.Key terms are boldfaced and defined in context throughout each chapter,and Key Concept boxes at regular intervals recap the central ideas.General interes boxes provide interesting excursions into related topics and highlight real-world studies that use the methods or concepts being discussed in the text.A Summary concluding each chapter serves as a helpful framework for reviewing the mair points of coverage. Available for student practice or instructor assignment in mvlab Economics are Review the Concepts questions,Exercises,and Empirical Exercises from the text.These questions and exercises are auto-graded,giv ing students practical hands-on experience with solving problems using the data sets used in the text. .100 percent of Review the Concepts questions are available in MyLab. .Select Exercises and Empirical Exercises are available in MyLab.Many of the Empirical Exercises are algorithmic and based on the data sets used in the text. These exercises require students to use excel or an econometrics software pack. age to analyze the data and derive results New to the 4th edition are concept exercises that focus on core concepts and economic interpretations.Many are algorithmic and include the Help Me Solve This learning aid Contemporary Choice of Topics The topics we cover reflect the best of contemporary applied econometrics One can only do so much in an introductory course.so we focus on procedures and tests that are commonly (or increasingly)used in practice.For example: Instrumental variables regression.We present instrumental variables regres sion as a general method for handling correlation between the error term and a regressor,which can arise for many reasons,including omitted variables and simultaneous causality.The two assumptions for a valid instrument- exogeneity and relevance-are given equal billing.We follow that presentation with an extended discussion of where instruments come from and with tests of overidentifving restrictions and diagnostics for weak instruments,and we explain what to do if these diagnostics suggest problems. Program evaluation.Many modern econometric studies analyze either ran domized controlled experiments or quasi-experiments,also known as natural experiments.We address these topics,often collectively referred to as program
data, which are provided on the text’s Companion Website (www.pearsonglobaleditions .com) and in MyLab Economics. Throughout the text, we have focused on helping students understand, retain, and apply the essential ideas. Chapter introductions provide real-world grounding and motivation, as well as brief road maps highlighting the sequence of the discussion. Key terms are boldfaced and defined in context throughout each chapter, and Key Concept boxes at regular intervals recap the central ideas. General interest boxes provide interesting excursions into related topics and highlight real-world studies that use the methods or concepts being discussed in the text. A Summary concluding each chapter serves as a helpful framework for reviewing the main points of coverage. Available for student practice or instructor assignment in MyLab Economics are Review the Concepts questions, Exercises, and Empirical Exercises from the text. These questions and exercises are auto-graded, giving students practical hands-on experience with solving problems using the data sets used in the text. • 100 percent of Review the Concepts questions are available in MyLab. • Select Exercises and Empirical Exercises are available in MyLab. Many of the Empirical Exercises are algorithmic and based on the data sets used in the text. These exercises require students to use Excel or an econometrics software package to analyze the data and derive results. • New to the 4th edition are concept exercises that focus on core concepts and economic interpretations. Many are algorithmic and include the Help Me Solve This learning aid. Contemporary Choice of Topics The topics we cover reflect the best of contemporary applied econometrics. One can only do so much in an introductory course, so we focus on procedures and tests that are commonly (or increasingly) used in practice. For example: • Instrumental variables regression. We present instrumental variables regression as a general method for handling correlation between the error term and a regressor, which can arise for many reasons, including omitted variables and simultaneous causality. The two assumptions for a valid instrument— exogeneity and relevance—are given equal billing. We follow that presentation with an extended discussion of where instruments come from and with tests of overidentifying restrictions and diagnostics for weak instruments, and we explain what to do if these diagnostics suggest problems. • Program evaluation. Many modern econometric studies analyze either randomized controlled experiments or quasi-experiments, also known as natural experiments. We address these topics, often collectively referred to as program 30 Preface A01_STOC4455_04_GE_FM.indd 30 06/12/18 5:50 PM
Preface evaluation.in Chapter 13.We present this research strategy as an alternative approach to the problems of omitted variables.simultaneous causality.and selection,and we assess both the strengths and the weaknesses of studies using experimental or quasi-experimental data. ·Prediction with“big data.”Chapter 14 takes up the opportunities and challenges posed by large cross-sectional data sets.An increasingly common application in econometrics is making predictions when the number of pre- dictors is very large.This chapter focuses on methods designed to use many predictors in a way that produces accurate and precise out-of-sample predic tions.The chapter covers some of the building blocks of machine learning and the methods can substantially improve upon OLs when the number of predictors is large.In addition,these methods extend to nonstandard data such as text data. Forecasting.The chapter on forecasting (Chapter 15)considers univariate (autoregressive)and multivariate forecasts using time series regression.not large structural models We focuson simple and reliable tools,such as autoregressions and model selection via an information criterion that work well in practice.This chapter also features a practically oriented treat- ment of structural breaks(at known and unknown dates)and pseudo out-of- sample forecasting,all in the context of developing stable and reliable time series forecasting models. Time series regression.The chapter on causal inference using time series data (Chapter 16)pays careful attention to when different estimatior methods,including generalized least squares,will or will not lead to valid causal inferences and when it is advisable to estimate dynamic regressions using OLS with heteroskedasticity-and autocorrelation-consistent stan- dard errors. Theory That Matches Applications Although econometric tools are best motivated by empirical applications,students need to learn enough econometric theory to understand the strengths and limita tions of those tools.We provide a modern treatment in which the fit between theory and applications is as tight as possible,while keeping the mathematics at a level that empirical applications share some common characteristics:The dat sets typically have many observations(hundreds or more):regressors are not fixed over repeated samples but rather are collected by random sampling(or some other mechanism that makes them random):the distributed and there is no a priori reason to think that the errors are homoskedastic(although often there are reasons to think that they are heteroskedastic)
evaluation, in Chapter 13. We present this research strategy as an alternative approach to the problems of omitted variables, simultaneous causality, and selection, and we assess both the strengths and the weaknesses of studies using experimental or quasi-experimental data. • Prediction with “big data.” Chapter 14 takes up the opportunities and challenges posed by large cross-sectional data sets. An increasingly common application in econometrics is making predictions when the number of predictors is very large. This chapter focuses on methods designed to use many predictors in a way that produces accurate and precise out-of-sample predictions. The chapter covers some of the building blocks of machine learning, and the methods can substantially improve upon OLS when the number of predictors is large. In addition, these methods extend to nonstandard data, such as text data. • Forecasting. The chapter on forecasting (Chapter 15) considers univariate (autoregressive) and multivariate forecasts using time series regression, not large simultaneous equation structural models. We focus on simple and reliable tools, such as autoregressions and model selection via an information criterion, that work well in practice. This chapter also features a practically oriented treatment of structural breaks (at known and unknown dates) and pseudo out-ofsample forecasting, all in the context of developing stable and reliable time series forecasting models. • Time series regression. The chapter on causal inference using time series data (Chapter 16) pays careful attention to when different estimation methods, including generalized least squares, will or will not lead to valid causal inferences and when it is advisable to estimate dynamic regressions using OLS with heteroskedasticity- and autocorrelation-consistent standard errors. Theory That Matches Applications Although econometric tools are best motivated by empirical applications, students need to learn enough econometric theory to understand the strengths and limitations of those tools. We provide a modern treatment in which the fit between theory and applications is as tight as possible, while keeping the mathematics at a level that requires only algebra. Modern empirical applications share some common characteristics: The data sets typically have many observations (hundreds or more); regressors are not fixed over repeated samples but rather are collected by random sampling (or some other mechanism that makes them random); the data are not normally distributed; and there is no a priori reason to think that the errors are homoskedastic (although often there are reasons to think that they are heteroskedastic). Preface 31 A01_STOC4455_04_GE_FM.indd 31 06/12/18 10:52 AM
32 Preface These observations lead to important differences between the theoretical devel- opment in this text and other texts: .Large-sample approach.Because data sets are large.from the outset we use large-sample normal approximations to sampling distributions for hypothesis testing and confidence intervals.In our experience.it takes less time to teach the rudiments of large-sample approximations than to teach the Studentt and exact ons,degrees-of-freedom corrections,and so forth.This large-sample approach also saves students the frustration of discovering that,because of nonnormal errors,the exact distribution theory they just mastered is irrelevant. Once taught in the context of the sample mean,the large-sample approach to hypothesis testing and confidence intervals carries directly through multiple regression analysis,logit and probit,instrumental variables estimation,and time series methods. Random sampling.Because regressors are rarely fixed in econometric applica tions,from the outset we treat data on all variables (dependent and indepen dent)as the result of random sampling.This assumption matches our initial applications to cross-sectional data,it extends readily to panel and time series data,and because of our large-sample approach,it poses no additional concep- tual or mathematical difficulties. Heteroskedasticiry.Applied econometricians routinely use heteroskedasticity- robust standard errors toeliminate worries about whether heteroskedasticity is present or not.In this book,we move beyond treating heteroskedasticity as an exception or a"problem"to be"solved":instead,we allow for heteroskedastic- ity from the outset and simply use heteroskedasticity-robust standard errorsWe present homoskedasticity as a special case that provides a theoretical motivation for OLS. Skilled Producers,Sophisticated Consumers We hope that students using this book will become sophisticated consumers of empirical analysis.To do so,they must learn not only how to use the tools of regres- sion analysis but also how to assess the validity of empirical analyses presented to them Our approach to teaching how to assess an empirical study is threefold.First, immediately after introducing the main tools of regression analysis,we devote Chapter 9 to the threats to internal and external validity of an empirical study.This chapter discusses data problems and issues of generalizing findings to other settings It also examines the main threats to regression analysis,including omitted variables. functional form misspecification.errors-in-variables.selection.and simultaneity- and ways to recognize these threats in practice
These observations lead to important differences between the theoretical development in this text and other texts: • Large-sample approach. Because data sets are large, from the outset we use large-sample normal approximations to sampling distributions for hypothesis testing and confidence intervals. In our experience, it takes less time to teach the rudiments of large-sample approximations than to teach the Student t and exact F distributions, degrees-of-freedom corrections, and so forth. This large-sample approach also saves students the frustration of discovering that, because of nonnormal errors, the exact distribution theory they just mastered is irrelevant. Once taught in the context of the sample mean, the large-sample approach to hypothesis testing and confidence intervals carries directly through multiple regression analysis, logit and probit, instrumental variables estimation, and time series methods. • Random sampling. Because regressors are rarely fixed in econometric applications, from the outset we treat data on all variables (dependent and independent) as the result of random sampling. This assumption matches our initial applications to cross-sectional data, it extends readily to panel and time series data, and because of our large-sample approach, it poses no additional conceptual or mathematical difficulties. • Heteroskedasticity. Applied econometricians routinely use heteroskedasticityrobust standard errors to eliminate worries about whether heteroskedasticity is present or not. In this book, we move beyond treating heteroskedasticity as an exception or a “problem” to be “solved”; instead, we allow for heteroskedasticity from the outset and simply use heteroskedasticity-robust standard errors. We present homoskedasticity as a special case that provides a theoretical motivation for OLS. Skilled Producers, Sophisticated Consumers We hope that students using this book will become sophisticated consumers of empirical analysis. To do so, they must learn not only how to use the tools of regression analysis but also how to assess the validity of empirical analyses presented to them. Our approach to teaching how to assess an empirical study is threefold. First, immediately after introducing the main tools of regression analysis, we devote Chapter 9 to the threats to internal and external validity of an empirical study. This chapter discusses data problems and issues of generalizing findings to other settings. It also examines the main threats to regression analysis, including omitted variables, functional form misspecification, errors-in-variables, selection, and simultaneity— and ways to recognize these threats in practice. 32 Preface A01_STOC4455_04_GE_FM.indd 32 06/12/18 10:52 AM
Preface 33 Second,we apply these methods for assessing empirical studies to the empirical analysis of the ongoing examples in the book.We do so by considering alternative specifications and by systematically addressing the various threats to validity of the analyses presented in the book. Third,to become sophisticated consumers.students need firsthand experienc as producers.Active learning beats passive learning.and econometrics is an ideal course for active learning.For this reason,the MyLab Economics and text web- site feature data sets,software,and suggestions for empirical exercises of different scopes Approach to Mathematics and Level of Rigor Our aim is for students to develop a sophisticated understanding of the tools of moder regresion analysis whethe er the course is taught at a"high"or a"low"level of mathematics.Parts I through IV of the text (which cover the substantive material) are written for students with only precalculus mathematics.Parts I through IV have fewer equations and more applications than many introductory econometrics books and far fewer equations than books aimed at mathematical sections of undergradu ate courses.But more equations do not imply a more sophisticated treatment.In our experience.a more mathematical treatment does not lead to a deeper understanding for most stude That said,different students learn differently,and for mathematically well- orepared students.learning can be enhanced by a more explicit mathematical .The appendices in Parts IV therefore provide key calculations that are too involved to be included in the text.In addition,Part Vcontains an intro duction to econometric theory that is appropriate for students with a stronger mathematical background.When the mathematical chapters in Part V are used in conjunction with the material in Parts I through IV(including appendices) this book is suitable for advanced undergraduate or master's level econometrics courses. Developing Career Skills For students to succeed in a rapidly changing job market.they should be aware of their career options and how to go about developing a variety of skills.Data analysis is an increasingly marketable skill.This text prepares the students for a range of data analytic applications,including causal inference and prediction. It also introduces the students to the core concepts of prediction using large data sets
Second, we apply these methods for assessing empirical studies to the empirical analysis of the ongoing examples in the book. We do so by considering alternative specifications and by systematically addressing the various threats to validity of the analyses presented in the book. Third, to become sophisticated consumers, students need firsthand experience as producers. Active learning beats passive learning, and econometrics is an ideal course for active learning. For this reason, the MyLab Economics and text website feature data sets, software, and suggestions for empirical exercises of different scopes. Approach to Mathematics and Level of Rigor Our aim is for students to develop a sophisticated understanding of the tools of modern regression analysis, whether the course is taught at a “high” or a “low” level of mathematics. Parts I through IV of the text (which cover the substantive material) are written for students with only precalculus mathematics. Parts I through IV have fewer equations and more applications than many introductory econometrics books and far fewer equations than books aimed at mathematical sections of undergraduate courses. But more equations do not imply a more sophisticated treatment. In our experience, a more mathematical treatment does not lead to a deeper understanding for most students. That said, different students learn differently, and for mathematically wellprepared students, learning can be enhanced by a more explicit mathematical treatment. The appendices in Parts I-IV therefore provide key calculations that are too involved to be included in the text. In addition, Part V contains an introduction to econometric theory that is appropriate for students with a stronger mathematical background. When the mathematical chapters in Part V are used in conjunction with the material in Parts I through IV (including appendices), this book is suitable for advanced undergraduate or master’s level econometrics courses. Developing Career Skills For students to succeed in a rapidly changing job market, they should be aware of their career options and how to go about developing a variety of skills. Data analysis is an increasingly marketable skill. This text prepares the students for a range of data analytic applications, including causal inference and prediction. It also introduces the students to the core concepts of prediction using large data sets. Preface 33 A01_STOC4455_04_GE_FM.indd 33 06/12/18 10:52 AM
34 Preface Table of Contents Overview There are five parts to Introduction to Econometrics.This text assumes that the stu- dent has had a course in probability and statistics,although we review that material in part i we cover the core material of regression analysis in part il parts iil iv and Vpresent additional topics that build on the core treatment in Part II. PartI Chapter 1 introduces econometrics and stresses the importance of providing quanti- tative answers to quantitative questions It discusses the concept of causality in sta- tistical studies and surveys the different types of data encountered in econometrics. Material from probability and statistics is reviewed in Chapters 2 and 3,respectively; whether these chapters are taught in a given course or are simply provided as a refer- ence depends on the background of the students Part Il Chapter 4 introduces regression with a single regressor and ordinary least squares (OLS)estimation,and Chapter 5 discusses hypothesis tests and confidence intervals in the regression model with a single regressor.In Chapter 6,students learn how they can address omitted variable bias using multiple regression,thereby estimating the effect of one independent variable while holding other independent variables con- stant.Chapter 7 covers hypothesis tests including F-tests,and confidence intervals in multiple regression.In Chapter 8,the linear regression model is extended to models with nonlinear population regression functions,with a focus on regression functions that are linear in the parameters(so that the parameters can be estimated by OLS).In Chapter9,students step back and learn how to identify the strengths and limitations of regression studies,seeing in the process how to apply the concepts of internal and external validity. Part Ill Part III presents extensions of regression methods.In Chapter 10,students learn how to use panel data to control for unobserved variables that are constant over time.Chapter 11 covers regression with a binary dependent variable.Chapter 12 shows how instrumental variables regression can be used to address a variety of problems that produce correlation between the error term and the regressor.and examines how one might find and evaluate valid instruments.Chapter 13 introduces students to the analysis of data from experiments and quasi-,or natural,experiments topics often referred to as "program evaluation."Chapter 14 turns to econometric issues that arise with large data sets,and focuses on prediction when there are very many predictors
Table of Contents Overview There are five parts to Introduction to Econometrics. This text assumes that the student has had a course in probability and statistics, although we review that material in Part I. We cover the core material of regression analysis in Part II. Parts III, IV, and V present additional topics that build on the core treatment in Part II. Part I Chapter 1 introduces econometrics and stresses the importance of providing quantitative answers to quantitative questions. It discusses the concept of causality in statistical studies and surveys the different types of data encountered in econometrics. Material from probability and statistics is reviewed in Chapters 2 and 3, respectively; whether these chapters are taught in a given course or are simply provided as a reference depends on the background of the students. Part II Chapter 4 introduces regression with a single regressor and ordinary least squares (OLS) estimation, and Chapter 5 discusses hypothesis tests and confidence intervals in the regression model with a single regressor. In Chapter 6, students learn how they can address omitted variable bias using multiple regression, thereby estimating the effect of one independent variable while holding other independent variables constant. Chapter 7 covers hypothesis tests, including F-tests, and confidence intervals in multiple regression. In Chapter 8, the linear regression model is extended to models with nonlinear population regression functions, with a focus on regression functions that are linear in the parameters (so that the parameters can be estimated by OLS). In Chapter 9, students step back and learn how to identify the strengths and limitations of regression studies, seeing in the process how to apply the concepts of internal and external validity. Part III Part III presents extensions of regression methods. In Chapter 10, students learn how to use panel data to control for unobserved variables that are constant over time. Chapter 11 covers regression with a binary dependent variable. Chapter 12 shows how instrumental variables regression can be used to address a variety of problems that produce correlation between the error term and the regressor, and examines how one might find and evaluate valid instruments. Chapter 13 introduces students to the analysis of data from experiments and quasi-, or natural, experiments, topics often referred to as “program evaluation.” Chapter 14 turns to econometric issues that arise with large data sets, and focuses on prediction when there are very many predictors. 34 Preface A01_STOC4455_04_GE_FM.indd 34 06/12/18 10:52 AM