Introduction to Econometrics, 2/E
James H. Stock, Harvard University
Mark W. Watson, Princeton University

ISBN-10: 0321278879
ISBN-13: 9780321278876

Publisher: Addison-Wesley
Copyright: 2007
Format: Cloth; 840 pp
Published: 07/21/2006

Suggested retail price: $160.00
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Designed for a first course in introductory econometrics, Introduction to Econometrics, reflects modern theory and practice, with interesting applications that motivate and match up with the theory to ensure students grasp the relevance of econometrics. Authors James H. Stock and Mark W. Watson integrate real-world questions and data into the development of the theory, with serious treatment of the substantive findings of the resulting empirical analysis.

  • Real-world questions and data: All examples can be understood by students with an introductory economics background, so the instructor can focus on teaching econometrics, not microeconomics or macroeconomics.
  • Contemporary choice of topics: Stock and Watson focus on the procedures and tests commonly used in modern practice. For example:
  • Instrumental variables regression is presented as a general method for handling correlation between the error term and a regressor, which can arise for many reasons, including simultaneous causality. The two assumptions for a valid instrument–exogeneity and relevance–are given equal billing.
  • Program evaluation (covered in Chapter 11)includes a discussion of the strengths and weaknesses of randomized controlled experiments and natural experiments as alternative approaches to the problems of omitted variables, simultaneous causality, and selection.
  • Forecasting (Chapter 12)considers univariate (autoregressive) and multivariate forecasts using time series regression, not large simultaneous equation structural models.
  • Time series regression coverage makes a clear distinction between two very different applications of time series regression: forecasting and estimation of dynamic causal effects.
  • Theory that matches applications: This modern treatment of econometrics fits theory and applications together as tightly as possible. The three key differences that set the theoretical development of this text apart are a large sample approach that saves students time and frustration, treatment of data on all variables as the result of random sampling, and early use of heteroskedasticity rather than treating it as a problem to be solved.
  • Intuitive explanations: Praised by reviewers, the text features common-sense explanations that emphasize conceptual approaches to understanding and that facilitate students' mastery of both basic and technical topics.
  • Pedagogical features: Key Concept boxes throughout the text highlight and reinforce essential ideas. General-interest sidebars provide additional interesting real-world examples closely tied to the central ideas.
  • Accessible mathematics: Parts I-IV of the textbook, which cover all the core substantive material, are accessible to students with only precalculus mathematics.
  • Advanced topics: Part V, on introductory econometric theory, is suitable for those teaching a more rigorous basic course and for those teaching mathematical sections of undergraduate courses or master’s level econometrics courses.
  • Companion Website: Web site features include data sets, projects, software tutorials, suggested empirical exercises of differing scopes, and more.

  • Coverage of core regression material in Part II is expanded to four chapters. .
  • Chapter 4, Linear Regression with One Regressor, focuses solely on Ordinary Least Squares (OLS) estimation and assumptions.
  • Chapter 5, Hypothesis Tests and Confidence Intervals in the Single-Regressor Model, focuses on inference using OLS, including an expanded discussion of heteroskedasticity and homoskedasticity, a discussion of the t-distribution, and an expanded discussion of motivation for using OLS.
  • Chapter 6, Linear Regression with Multiple Regressors includes an expanded discussion of multicollniearity.
  • Chapter 7, Hypothesis Testing and Confidence Intervals in the Multiple Regression Model, includes inference using exact F-distribution. The homoskedasticity-only F-statistic formula, formerly in the Appendix, is now included and discussed in the text chapter.
  • Part IV, Regression Analysis of Economic Time Series Data, is redesigned to allow instructors the flexibility to move directly to it following Part II, Fundamentals of Regression Analysis.
  • Additional empirical examples appear throughout, including estimation of the value of an education, forecasting the stock market, and an estimation of the marginal propensity to consume using the 2001 US Income Tax Rebate.
  • An increased number of end-of-chapter exercises offer intensive practice working with the concepts and techniques introduced in the chapter.

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PART ONE: INTRODUCTION AND REVIEW

Chapter 1 Economic Questions and Data

Chapter 2 Review of Probability

Chapter 3 Review of Statistics
 

PART TWO: FUNDAMENTALS OF REGRESSION ANALYSIS

Chapter 4 Linear Regression with One Regressor

Chapter 5 Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals

Chapter 6 Linear Regression with Multiple Regressors

Chapter 7 Hypothesis Tests and Confidence Intervals in Multiple Regression

Chapter 8 Nonlinear Regression Functions

Chapter 9 Assessing Studies Based on Multiple Regression

 

PART THREE: FURTHER TOPICS IN REGRESSION ANALYSIS

Chapter 10 Regression with Panel Data

Chapter 11 Regression with a Binary Dependent Variable

Chapter 12 Instrumental Variables Regression

Chapter 13 Experiments and Quasi-Experiments


PART FOUR: REGRESSION ANALYSIS OF ECONOMIC TIME SERIES DATA

Chapter 14 Introduction to Time Series Regression and Forecasting

Chapter 15 Estimation of Dynamic Causal Effects

Chapter 16 Additional Topics in Time Series Regression

 

PART FIVE: THE ECONOMETRIC THEORY OF REGRESSION ANALYSIS

Chapter 17 The Theory of Linear Regression with One Regressor

Chapter 18 The Theory of Multiple Regression

 

Appendix: Statistical Tables

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