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.
PART ONE INTRODUCTION AND REVIEWChapter 1 Economic Questions and Data1.1 Economic Questions We Examine1.2 Causal Effects and Idealized Experiments1.3 Data: Sources and TypesChapter 2 Review of Probability2.1 Random Variables and Probability Distributions2.2 Expected Values, Mean, and Variance2.3 Two Random Variables2.4 The Normal, Chi-Squared, Studentt, and F Distributions2.5 Random Sampling and the Distribution of the Sample Average2.6 Large-Sample Approximations to Sampling DistributionsChapter 3 Review of Statistics3.1 Estimation of the Population Mean3.2 Hypothesis Tests Concerning the Population Mean3.3 Confidence Intervals for the Population Mean3.4 Comparing Means from Different Populations3.5 Differences-of-Means Estimation of Causal Effects3.6 Using the t-Statistic When the Sample Size Is Small3.7 Scatterplot, the Sample Covariance, and the Sample Correlation Using Experimental DataPART TWO FUNDAMENTALS OF REGRESSION ANALYSISChapter 4 Linear Regression with One Regressor4.1 The Linear Regression Model4.2 Estimating the Coefficients of the Linear Regression Model4.3 Measures of Fit4.5 The Sampling Distribution of the OLS Estimators4.6 ConclusionChapter 5 Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals5.1 Testing Hypotheses About One of the Regression Coefficients5.2 Confidence Intervals for a Regression Coefficient5.3 Regression When X Is a Binary Variable5.5 The Theoretical Foundations of Ordinary Least Squares5.5 The Theoretical Foundations of Ordinary Least Squares5.6 Using the t-Statistic in Regression When the Sample Size Is Small5.7 ConclusionChapter 6 Linear Regression with Multiple Regressors6.1 Omitted Variable Bias6.2 The Multiple Regression Model6.3 The OLS Estimator in Multiple Regression6.4 Measures of Fit in Multiple Regression6.5 The Least Squares Assumptions in Multiple Regression6.6 The Distribution of the OLS Estimators6.7 Multicollinearity6.8 ConclusionChapter 7 Hypothesis Tests and Confidence Intervals in Multiple Regression7.1 Hypothesis Tests and Confidence Intervals for a Single Coefficient7.2 Tests of Joint Hypotheses7.3 Testing Single Restrictions Involving Multiple Coefficients7.4 Confidence Sets for Multiple Coefficients7.6 Analysis of the Test Score Data Set7.7 ConclusionChapter 8 Nonlinear Regression Functions8.1 A General Strategy for Modeling Nonlinear Regression Functions8.2 Nonlinear Functions of a Single Independent Variable8.4 Nonlinear Effects on Test Scores of the Student-Teacher Ratio8.5 ConclusionChapter 9 Assessing Studies Based on Multiple Regression9.1 Internal and External Validity9.2 Threats to Internal Validity of Multiple Regression Analysis9.3 Internal and External Validity When the Regression Is Used for Forecasting9.4 Example: Test Scores and Class Size9.5 ConclusionChapter 10 Conducting a Regression Study Using Economic Data10.1 Choosing a Topic10.2 Collecting the Data10.3 Conducting Your Regression Analysis10.4 Writing Up Your ResultsAppendixReferencesAnswers to "Review the Concepts" QuestionsGlossaryIndex
Om bidragsyterneJames 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.