Introductory Econometrics: A Modern Approach

Citation

Wooldridge, J. M. (2006). Introductory Econometrics: A Modern Approach (3rd ed.). Thomson/South-Western.

Chapter Summary

The third edition (2006) runs to about 890 pages. It has 19 chapters in three parts: regression analysis with cross-section data (Chapters 2 to 9), regression analysis with time series data (Chapters 10 to 12) and advanced topics (Chapters 13 to 19). It opens with a chapter on the nature of econometrics and economic data. The publisher's synopsis says the book aims to bridge the gap between the mechanics of econometrics and its modern applications. It does this through a systematic approach motivated by the major problems facing applied researchers. It also emphasises examples that give concrete reality to economic relationships and allow policy questions to be treated in an accessible framework.

Chapter 1: The Nature of Econometrics and Economic Data

The opening chapter introduces what econometrics is and the kinds of economic data that later chapters analyse. It sets up the vocabulary used for the three parts.

Part I: Regression Analysis with Cross-Section Data

Chapter 2: The Simple Regression Model. The starting point: regression with a single explanatory variable.

Chapter 3: Multiple Regression Analysis: Estimation. Estimation extended to several explanatory variables.

Chapter 4: Multiple Regression Analysis: Inference. Statistical inference for multiple regression.

Chapter 5: Multiple Regression Analysis: OLS Asymptotics. The large-sample properties of ordinary least squares.

Chapter 6: Multiple Regression Analysis: Further Issues. Additional practical issues in using multiple regression.

Chapter 7: Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables. Including qualitative information in regressions through dummy variables.

Chapter 8: Heteroskedasticity. What happens when error variances are not constant, and how to respond.

Chapter 9: More on Specification and Data Problems. Specification choices and problems in the data.

Part II: Regression Analysis with Time Series Data

Chapter 10: Basic Regression Analysis with Time Series Data. Regression when observations are ordered in time.

Chapter 11: Further Issues in Using OLS with Time Series Data. Complications specific to time series.

Chapter 12: Serial Correlation and Heteroskedasticity in Time Series Regressions. Errors that are correlated over time or have changing variance in a time series setting.

Part III: Advanced Topics

Chapter 13: Pooling Cross Sections across Time: Simple Panel Data Methods. Combining cross-sections from different periods and the first panel-data methods.

Chapter 14: Advanced Panel Data Methods. A more developed treatment of panel data.

Chapter 15: Instrumental Variables Estimation and Two Stage Least Squares. Estimation when an explanatory variable is correlated with the error term.

Chapter 16: Simultaneous Equations Models. An introduction to systems in which variables are determined jointly.

Chapter 17: Limited Dependent Variable Models and Sample Selection Corrections. Models for outcomes with restricted range, and corrections for non-random samples.

Chapter 18: Advanced Time Series Topics. Further topics in time series analysis.

Chapter 19: Carrying Out an Empirical Project. The closing chapter, on carrying out an empirical research project of one's own.

Key Concepts

Chapters 1–6

• Simple and multiple regression: estimating relationships between an outcome and one or several explanatory variables.
• Inference: testing hypotheses and building confidence statements about regression coefficients.
• OLS asymptotics: properties of ordinary least squares as the sample grows.

Chapters 7–9

• Binary (dummy) variables: representing qualitative information in a regression.
• Heteroskedasticity: non-constant error variance.
• Specification and data problems: the practical issues named in Chapter 9.

Chapters 10–12

• Time series regression: regression with data ordered over time.
• Serial correlation: correlation of errors across time.

Chapters 13–19

• Pooled cross-sections and panel data: data that follow units or combine samples over time.
• Instrumental variables and two stage least squares: methods for handling regressors that are correlated with the error.
• Simultaneous equations models: jointly determined variables.
• Limited dependent variables and sample selection: models for restricted outcomes and non-random samples.
• Empirical project: the practical skills that tie the methods to research.

Critical Analysis

Strengths

• Organisation by data type. The book separates cross-section regression, time series regression and advanced topics. This gives a clear map, so a reader can see where each method belongs.
• Examples-first design. The publisher's synopsis says the book emphasises examples that give concrete reality to economic relationships. These examples also let it treat interesting policy questions in an accessible framework.
• A place for advanced methods. Panel data, instrumental variables, simultaneous equations, limited dependent variables and advanced time series each have their own chapters in Part III.
• A chapter on carrying out a project. The closing chapter is devoted to carrying out an empirical project, so the book ends by turning the methods toward students' own research.
• Still a current course text. The UNC Fall 2026 econometrics course (see Common course assignments below) lists Wooldridge's Introductory Econometrics as the required textbook and builds its whole outline around the book's chapters.

Limitations

• Edition. The third edition was published in 2006, and later editions exist. Students should check which edition their course uses, since chapter content and numbering may differ. For example, the UNC Fall 2026 course accepts the 6th, 7th or 8th edition, not the 3rd edition listed here.
• Length. At about 890 pages, the book covers more than one term. Part III is a menu from which a course picks topics. The UNC Fall 2026 course, for instance, covers the cross-section core plus a single Part III chapter (instrumental variables) and treats further topics as "time permitting."
• Treatment of causal designs. In this edition, instrumental variables appear in Chapter 15 and panel methods in Chapters 13 and 14. There are no dedicated chapters on difference-in-differences or regression discontinuity. These newer causal-inference designs are better learned from the more recent books in the course.

Edition notes

UNC Fall 2026 allows the 6th, 7th or 8th edition, and the chapter numbers in the course list below follow that syllabus. This guide is based on the 2006 printing (Thomson/South-Western, ISBN 978-0-324-28978-7), so match chapter titles to your copy. For the topics the course lists, the core chapters are stable across recent editions:

• Chapters 2–8 (simple regression through heteroskedasticity)
• Chapter 15 (instrumental variables)

These line up by number with the 2006 contents above.

Outside that core, rely on titles rather than numbers:

• Panel data. The UNC syllabus lists "Ch 10 Panel Data." In the 2006 printing, Chapter 10 is Basic Regression Analysis with Time Series Data, and panel methods appear in Chapters 13 and 14.
• Sample selection. The syllabus's "Models with Selection" corresponds most closely to the sample-selection material in the 2006 Chapter 17, Limited Dependent Variable Models and Sample Selection Corrections.

Where It Fits in the Course

In the econometrics list, Wooldridge is a full-length textbook that treats regression thoroughly across cross-section, time series and panel data. It can serve as the spine of a one-semester undergraduate course, as in the UNC Fall 2026 course described below. There it is the required text and the course moves through its chapters in order, from simple regression to instrumental variables.

The other books on the list play different roles:

• Studenmund, Using Econometrics: a gentler, intuition-led introduction to single-equation regression.
• Newbold and colleagues, Statistics for Business and Economics: covers the statistical foundations beforehand.
• Stock and Watson, Introduction to Econometrics: the other standard undergraduate textbook, built around empirical questions and real data sets. Use it as a comparison or as a second source when a different explanation helps.

Angrist and Pischke's Mastering 'Metrics is an accessible introduction to the methods economists use to estimate causal effects. Their Mostly Harmless Econometrics is a compact guide to regression, instrumental variables, differences-in-differences and regression discontinuity. Both are good companions to this book, which supplies the systematic regression foundation they build on.

How to Use This Book

The book is for undergraduates and master's students in economics and the social sciences who want a systematic course in regression. An introductory statistics course and basic calculus are a sensible starting point.

A practical plan:

• Core: Chapters 1 to 4 and 6 to 8, which give the cross-section regression toolkit. Chapter 5 on asymptotics can be read lightly on a first pass, though some courses (UNC Fall 2026, below) include Chapter 5 in their outline.
• Second layer: Chapter 9, then Chapters 10 to 12 for time series.
• Choose from Part III: Chapters 13 to 15 on panel data and instrumental variables are the most widely useful. Chapters 16 to 18 are for particular needs.
• Finish with Chapter 19 and an empirical project of your own, using statistical software on a real data set.
• Alongside: a current statistical package and, for causal designs beyond instrumental variables, one of the shorter applied books above.

Common course assignments

UNC Econ 470-001 Econometrics (Fall 2026, Kédagni; syllabus) requires the 6th, 7th or 8th edition. Its outline, in order (chapter numbers as given in the syllabus):

1. Ch 2 Simple Linear Regression
2. Ch 3 Multiple Linear Regression
3. Ch 4 Inference
4. Ch 5 OLS Asymptotics
5. Ch 6 Further Issues
6. Ch 7 Dummy Variables
7. Ch 8 Heteroskedasticity
8. Ch 15 Instrumental Variables
9. Time permitting: Ch 10 Panel Data; Models with Selection

Three things stand out for a reader using the 2006 printing:

• The outline does not list Chapter 9 or the time-series chapters. It jumps from heteroskedasticity straight to instrumental variables.
• Chapters 2–8 and 15 match the 2006 numbering.
• For the "time permitting" topics, read the panel-data chapters (13–14 in the 2006 printing) and the sample-selection material in Chapter 17. Match by title, not by the syllabus's chapter number.

Focused reading and edition boundary

The trial below uses the companion problem. Open its assumptions and data before answering.

Reading route: 2006 printing, ISBN 9780324289787. Read basic regression and inference before the endogeneity and instrumental-variable material, and match your copy's own contents.

Try before continuing: Write down the outcome unit, the treatment unit, and the difference between a first stage and a reduced form. Checkpoint: A ratio with a nonzero denominator is numerically defined, but that does not prove its causal interpretation.

Edition and access: This page describes the 2006 printing. Later Cengage editions rearrange some material and update software examples, so if your course uses the 6th–8th edition (as the UNC Fall 2026 course above does), match chapter titles to your copy rather than relying on older page numbers.

Library catalogue entry for this edition