(eBook PDF) Introduction to Econometrics, 4th Global Edition – Digital Ebook – Instant Delivery Download

Product details:

• ISBN-10 ‏ : ‎ 1292264454
• ISBN-13 ‏ : ‎ 978-1292264455
• Author: James H. Stock, Mark W. Watson

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.
Table contents:

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

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