Introduction to Econometrics: Brief Edition (Addison-Wesley Series in Economics) von James H. Stock (Autor), Mark W. Watson (Autor)
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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.Features + BenefitsReal-world questions and data-the effect of reductions in class size on test scores...
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.
Features + Benefits
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 ofMultiple Regression focuses not only on mechanics and tools, but also on how those tools are useful because they address omitted variable bias.
By allowing forheteroskedasticity 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, Studentt, 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
Features + Benefits
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 ofMultiple Regression focuses not only on mechanics and tools, but also on how those tools are useful because they address omitted variable bias.
By allowing forheteroskedasticity 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, Studentt, 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