Description
PEARSON Introduction To Econometrics Global Edition 4Th Edition by James Stock
For courses in introductory econometrics.Engaging applications bring the theory and practice of modern econometrics to life Ensure students grasp the relevance of econometrics with Introduction to Econometrics - the text that connects modern theory and practice with motivating, engaging applications. The 4th Edition, Global Edition, maintains a focus on currency, while building on the philosophy that applications should drive the theory, not the other way around. The text incorporates real-world questions and data, and methods that are immediately relevant to the applications. With very large data sets increasingly being used in economics and related fields, a new chapter dedicated to Big Data helps students learn about this growing and exciting area. This coverage and approach make the subject come alive for students and helps them to become sophisticated consumers of econometrics. 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