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Learn more about modern Econometrics with this comprehensive introduction to the field, featuring engaging applications and bringing contemporary theories to life. Introduction to Econometrics, 4 th Edition , Global Edition by Stock and Watson is the ultimate introductory guide that connects modern theory with motivating, engaging applications.

The text ensures you get a solid grasp of this challenging subject's theoretical background, building on the philosophy that applications should drive the theory, not the other way around. The latest edition maintains the focus on currency, focusing on empirical analysis and incorporating real-world questions and data by using results directly relevant to the applications.

The text contextualises the study of Econometrics with a comprehensive introduction and review of economics, data, and statistics before proceeding to an extensive regression analysis studying the different variables and regression parameters. With a large data set increasingly used in Economics and related fields, a new chapter dedicated to Big Data will help you learn more about this growing and exciting area.

Sharing a variety of resources and tools to help your understanding and critical thinking of the topics introduced, such as General Interest boxes, or end-of-chapter, and empirical exercises and summaries, this industry-leading text will help you acquire a sophisticated knowledge of this fascinating subject.

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Autorentext

James Harold Stock is an American economist, professor of economics, and vice provost for climate and sustainability at Havard University.

Mark W. Watson is the Howard Harrison and Gabrielle Snyder Beck Professor of Economics and Public Affairs at Princeton University.

Inhalt

PART I: INTRODUCTION AND REVIEW

1. Economic Questions and Data

2. Review of Probability

3. Review of Statistics
   PART II: FUNDAMENTALS OF REGRESSION ANALYSIS

4. Linear Regression with One Regressor

5. Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals

6. Linear Regression with Multiple Regressors

7. Hypothesis Tests and Confidence Intervals in Multiple Regression

8. Nonlinear Regression Functions

9. Assessing Studies Based on Multiple Regression
   PART III: FURTHER TOPICS IN REGRESSION ANALYSIS

10. Regression with Panel Data

11. Regression with a Binary Dependent Variable

12. Instrumental Variables Regression

13. Experiments and Quasi-Experiments

14. Prediction with Many Regressors and Big Data
    PART IV: REGRESSION ANALYSIS OF ECONOMIC TIME SERIES DATA

15. Introduction to Time Series Regression and Forecasting

16. Estimation of Dynamic Causal Effects

17. Additional Topics in Time Series Regression
    PART V: THE ECONOMIC THEORY OF REGRESSION ANALYSIS

18. The Theory of Linear Regression with One Regressor

19. The Theory of Multiple Regression