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Summary
Summary
To make econometrics relevant in an introductory course, interesting applications must motivate the theory and the theory must match the applications. This text aims to motivate the need for tools with concrete applications, providing simple assumptions that match the application. This is a streamlined version of their text.
Author Notes
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.
Table of Contents
| Part 1 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 2 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 |