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by James H. Stock and Mark W. Watson

Cover type: Print On DemandEdition: 08

Copyright: 2008

Publisher: Addison-Wesley Longman, Inc.

Published: 2008

International: No

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KEY MESSAGE: 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 readers apply the theory immediately. Introduction to Econometrics, Brief, 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. Introduction and Review: Economic Questions and Data; Review of Probability; Review of Statistics. Fundamentals of Regression Analysis: Linear Regression with One Regressor; Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals in the Single-Regressor Model; Linear Regression with Multiple Regressors; Hypothesis Tests and Confidence Intervals in the Multiple Regressor Model; Nonlinear Regression Functions; Assessing Studies Based on Multiple Regression; Conducting a Regression Study Using Economic Data. MARKET For all readers interested in econometrics.

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,andFDistributions 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 thet-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 WhenXIs 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 Studentndash;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

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Summary

KEY MESSAGE: 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 readers apply the theory immediately. Introduction to Econometrics, Brief, 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. Introduction and Review: Economic Questions and Data; Review of Probability; Review of Statistics. Fundamentals of Regression Analysis: Linear Regression with One Regressor; Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals in the Single-Regressor Model; Linear Regression with Multiple Regressors; Hypothesis Tests and Confidence Intervals in the Multiple Regressor Model; Nonlinear Regression Functions; Assessing Studies Based on Multiple Regression; Conducting a Regression Study Using Economic Data. MARKET For all readers interested in econometrics.

Table of Contents

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,andFDistributions 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 thet-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 WhenXIs 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 Studentndash;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

Publisher Info

Publisher: Addison-Wesley Longman, Inc.

Published: 2008

International: No

Published: 2008

International: No

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,andFDistributions 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 thet-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 WhenXIs 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 Studentndash;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