In keeping with their successful introductory econometrics text, Stock and Watson motivate each methodological topic with a real-world policy application that uses data, so that students apply the theory immediately. Introduction to Econometrics, Brief Edition, is a streamlined version of their text, including the fundamental topics, an early review of statistics and probability, the core material of regression with cross-sectional data, and a capstone chapter on conducting empirical analysis.

• Real-world questions and data—the effect of reductions in class size on test scores and the returns to education—create a continuous platform that motivates important theoretical ideas, and link student understanding to real-world empirical applications.
• Contemporary theory and practice are reflected in the choice of topics, focusing on the procedures and tests commonly used in modern practice.
• An opening review of statistics and probability emphasizes sampling variability, sampling distributions, and how sampling uncertainty is handled using the methods of statistical inference.
• Treatment of Multiple Regression focuses not only on mechanics and tools, but also on how those tools are useful because they address omitted variable bias.
• By allowing for heteroskedasticity from the outset, students have an easier time understanding the method and instructors are able to apply the theory earlier in the course.
• Core regression material in Part II is presented in four, carefully developed chapters:
• Chapter 4, Linear Regression with One Regressor, focuses solely on Ordinary Least Squares (OLS) estimation and assumptions.
• Chapter 5, Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals, 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 Multiple Regression, includes inference using exact F-distribution and coverage of the homoskedasticity-only F-statistic formula.
• Chapter 10 focuses on conducting a regression study using economic data.
• The data sets in the empirical exercises are revisited in subsequent chapters, creating a continuous thread of analysis through the book.
• A level of mathematics appropriate for an introductory course is achieved by using fewer equations and more serious empirical applications, both in class and in homework exercises. Students need only an Algebra II course and an introductory course in statistics.
• Pedagogy helps students master the material, throughKey Concept boxes that highlight and reinforce essential ideas and sidebars that provide interesting real-world examples closely tied to the central ideas.
• Companion Website: The book’s Companion Website features include data sets, projects, software tutorials, suggested empirical exercises of differing scopes, and more. For more information, visit www.aw-bc.com/stock_watson.

PART ONE INTRODUCTION AND REVIEW
Chapter 1 Economic Questions and Data
1.1 Economic Questions We Examine
1.2 Causal Effects and Idealized Experiments
1.3 Data: Sources and Types

Chapter 2 Review of Probability
2.1 Random Variables and Probability Distributions
2.2 Expected Values, Mean, and Variance
2.3 Two Random Variables
2.4 The Normal, Chi-Squared, Student t, and F Distributions
2.5 Random Sampling and the Distribution of the Sample Average
2.6 Large-Sample Approximations to Sampling Distributions

Chapter 3 Review of Statistics
3.1 Estimation of the Population Mean
3.2 Hypothesis Tests Concerning the Population Mean
3.3 Confidence Intervals for the Population Mean
3.4 Comparing Means from Different Populations
3.5 Differences-of-Means Estimation of Causal Effects
3.6 Using the t-Statistic When the Sample Size Is Small
3.7 Scatterplot, the Sample Covariance, and the Sample Correlation Using Experimental Data

PART TWO FUNDAMENTALS OF REGRESSION ANALYSIS
Chapter 4 Linear Regression with One Regressor
4.1 The Linear Regression Model
4.2 Estimating the Coefficients of the Linear Regression Model
4.3 Measures of Fit
4.5 The Sampling Distribution of the OLS Estimators
4.6 Conclusion

Chapter 5 Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals
5.1 Testing Hypotheses About One of the Regression Coefficients
5.2 Confidence Intervals for a Regression Coefficient
5.3 Regression When X Is a Binary Variable
5.5 The Theoretical Foundations of Ordinary Least Squares
5.5 The Theoretical Foundations of Ordinary Least Squares
5.6 Using the t-Statistic in Regression When the Sample Size Is Small
5.7 Conclusion

Chapter 6 Linear Regression with Multiple Regressors
6.1 Omitted Variable Bias
6.2 The Multiple Regression Model
6.3 The OLS Estimator in Multiple Regression
6.4 Measures of Fit in Multiple Regression
6.5 The Least Squares Assumptions in Multiple Regression
6.6 The Distribution of the OLS Estimators
6.7 Multicollinearity
6.8 Conclusion

Chapter 7 Hypothesis Tests and Confidence Intervals in Multiple Regression
7.1 Hypothesis Tests and Confidence Intervals for a Single Coefficient
7.2 Tests of Joint Hypotheses
7.3 Testing Single Restrictions Involving Multiple Coefficients
7.4 Confidence Sets for Multiple Coefficients
7.6 Analysis of the Test Score Data Set
7.7 Conclusion

Chapter 8 Nonlinear Regression Functions
8.1 A General Strategy for Modeling Nonlinear Regression Functions
8.2 Nonlinear Functions of a Single Independent Variable
8.4 Nonlinear Effects on Test Scores of the Student–Teacher Ratio
8.5 Conclusion

Chapter 9 Assessing Studies Based on Multiple Regression
9.1 Internal and External Validity
9.2 Threats to Internal Validity of Multiple Regression Analysis
9.3 Internal and External Validity When the Regression Is Used for Forecasting
9.4 Example: Test Scores and Class Size
9.5 Conclusion

Chapter 10 Conducting a Regression Study Using Economic Data
10.1 Choosing a Topic
10.2 Collecting the Data
10.3 Conducting Your Regression Analysis
10.4 Writing Up Your Results

Appendix
References
Answers to “Review the Concepts” Questions
Glossary
Index

James Stock chairs the Department of Economics at Harvard University. His research focuses on empirical macroeconomics, forecasting, and econometric methods.  Among other things, he has served on the economics panel at the National Science Foundation, on the Academic Advisory Group of the Federal Reserve Bank of Boston, and as a consultant to the European Central Bank. He received his Bachelor’s degree from Yale and holds advanced degrees in statistics and economics from the University of California, Berkeley.

Mark Watson is the Howard Harrison and Gabrielle Snyder Beck Professor of Economics and Public Affairs at Princeton University and a research associate at the National Bureau of Economic Research. He is a fellow of the American Academy of Arts and Sciences and of the Econometric Society. His research focuses on time-series econometrics, empirical macroeconomics, and macroeconomic forecasting. He has served as a consultant for the Federal Reserve Banks of Chicago and Richmond. Before coming to Princeton, Watson served on the economics faculty at Harvard and Northwestern. Watson did his undergraduate work at Pierce Junior College and California State University at Northridge, completed his Ph.D. at the University of California at San Diego, and holds on honorary doctorate from the University of Bern.

Online Solutions Manual for Introduction to Econometrics, Brief Edition
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Stock & Watson
©2008  |  Prentice Hall  |  On-line Supplement  |  Live
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TestGen® Computerized Test Bank for Introduction to Econometrics, Brief Edition
Stock & Watson
©2008  |  Prentice Hall  |  On-line Supplement  |  Live
ISBN-10: 0321471164  |  ISBN-13: 9780321471161
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Companion Website for Introduction to Econometrics, Brief Edition
Stock & Watson
©2008  |  Prentice Hall  |  Website  |  Live
ISBN-10: 0321471261  |  ISBN-13: 9780321471260
This online resource is available at no cost at http://www.aw-bc.com/stock_watson.
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