발행 2025.01.31

Give students an understanding of how econometrics can answer questions in business, policy evaluation and forecasting with the practical approach found in Wooldridge’s "Introductory Econometrics: A Modern Approach, Cengage International Edition", 8th Edition.

Jeffrey M. Wooldridge

Jeffrey M. Wooldridge is University Distinguished Professor of Economics at Michigan State University, where he has taught since 1991. From 1986 to 1991, he was an assistant professor of economics at the Massachusetts Institute of Technology. He received his bachelor of arts, with majors in computer science and economics, from the University of California, Berkeley, in 1982, and received his doctorate in economics in 1986 from the University of California, San Diego. He has published more than 60 articles in internationally recognized journals, as well as several book chapters. He is also the author of Econometric Analysis of Cross Section and Panel Data, second edition. His awards include an Alfred P. Sloan Research Fellowship, the Plura Scripsit award from Econometric Theory, the Sir Richard Stone prize from the Journal of Applied Econometrics, and three graduate teacher-of-the-year awards from MIT. He is a fellow of the Econometric Society and of the Journal of Econometrics. He is past editor of the Journal of Business and Economic Statistics, and past econometrics coeditor of Economics Letters. He has served on the editorial boards of Econometric Theory, the Journal of Economic Literature, the Journal of Econometrics, the Review of Economics and Statistics, and the Stata Journal. He has also acted as an occasional econometrics consultant for Arthur Andersen, Charles River Associates, the Washington State Institute for Public Policy, Stratus Consulting, and Industrial Economics, Incorporated.

Chapter 1 The Nature of Econometrics and Economic Data 1

Part 1: Regression Analysis with Cross-Sectional Data 19

Chapter 2 The Simple Regression Model 20

Chapter 3 Multiple Regression Analysis: Estimation 67

Chapter 4 Multiple Regression Analysis: Inference 118

Chapter 5 Multiple Regression Analysis: OLS Asymptotics 167

Chapter 6 Multiple Regression Analysis: Further Issues 186

Chapter 7 Multiple Regression Analysis with Qualitative Information 229

Chapter 8 Heteroskedasticity 276

Chapter 9 More on Specification and Data Issues 309

Part 2: Regression Analysis with Time Series Data 347

Chapter 10 Basic Regression Analysis with Time Series Data 348

Chapter 11 Further Issues in Using OLS with Time Series Data 381

Chapter 12 Serial Correlation and Heteroskedasticity in Time Series Regressions 410

Part 3: Advanced Topics 441

Chapter 13 Pooling Cross Sections across Time. Simple Panel Data Methods 442

Chapter 14 Advanced Panel Data Methods 481

Chapter 15 Instrumental Variables Estimation and Two Stage Least Squares 515

Chapter 16 Simultaneous Equations Models 557

Chapter 17 Limited Dependent Variable Models and Sample Selection Corrections 583

Chapter 18 Advanced Time Series Topics 634

Chapter 19 Advanced Methods for Causal Inference 681

Chapter 20 Carrying Out an Empirical Project 731

Appendices

Math Refresher A Basic Mathematical Tools 755

Math Refresher B Fundamentals of Probability 773

Math Refresher C Fundamentals of Mathematical Statistics 803

Advanced Treatment D Summary of Matrix Algebra 838

Advanced Treatment E The Linear Regression Model in Matrix Form 849

Answers to Going Further Questions 866

Statistical Tables 875

References 882

Glossary 886

Index 903