# Introductory Econometrics: A Modern Approach 5th Edition Genres:

## Book Preface

My motivation for writing the first edition of Introductory Econometrics: A Modern Approach was that I saw a fairly wide gap between how econometrics is taught to undergraduates and how empirical researchers think about and apply econometric methods. I became convinced that teaching introductory econometrics from the perspective o professional users of econometrics would actually simplify the presentation, in addition to making the subject much more interesting. Based on the positive reactions to earlier editions, it appears that my hunch was correct.

Many instructors, having a variety of backgrounds and interests and teaching students with different levels of preparation, have embraced the modern approach to econometrics espoused in this text. The emphasis in this edition is still on applying econometrics to real- world problems. Each econometric method is motivated by a particular issue facing researchers analyzing nonexperimental data. The focus in the main text is on understanding and interpreting the assumptions in light of actual empirical applications: the mathematics required is no more than college algebra and basic probability and statistics.

Brief Contents

Chapter 1 The Nature of Econometrics and Economic Data 1
PART 1: Regression Analysis with Cross-Sectional Data 21
Chapter 2 The Simple Regression Model 22
Chapter 3 Multiple Regression Analysis: Estimation 68
Chapter 4 Multiple Regression Analysis: Inference 118
Chapter 5 Multiple Regression Analysis: OLS Asymptotics 168
Chapter 6 Multiple Regression Analysis: Further Issues 186
Chapter 7 Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables 227
Chapter 8 Heteroskedasticity 268
Chapter 9 More on Specification and Data Issues 303
PART 2: Regression Analysis with Time Series Data 343
Chapter 10 Basic Regression Analysis with Time Series Data 344
Chapter 11 Further Issues in Using OLS with Time Series Data 380
Chapter 12 Serial Correlation and Heteroskedasticity in Time Series Regressions 412
Chapter 13 Pooling Cross Sections Across Time: Simple Panel Data Methods 448
Chapter 14 Advanced Panel Data Methods 484
Chapter 15 Instrumental Variables Estimation and Two Stage Least Squares 512
Chapter 16 Simultaneous Equations Models 554
Chapter 17 Limited Dependent Variable Models and Sample Selection Corrections 583
Chapter 18 Advanced Time Series Topics 632
Chapter 19 Carrying Out an Empirical Project 676
Appendices
Appendix A Basic Mathematical Tools 703
Appendix B Fundamentals of Probability 722
Appendix C Fundamentals of Mathematical Statistics 755
Appendix D Summary of Matrix Algebra 796
Appendix E The Linear Regression Model in Matrix Form 807
Appendix F Answers to Chapter Questions 821
Appendix G Statistical Tables 831
References 838
Glossary 844
Index 862  PDFMay 30, 2020