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Introduction to Econometrics, 3/e


Introduction to Econometrics, 3/e
Author(s)  Simy Joy ,Payal Anand ,Priya Nair Rajeev
ISBN  9789352863501
Imprint  Pearson Education
Copyright  2018
Pages  840
Binding  Paperback
List Price  Rs. 1070.00
  
 
 

Introduction to Econometrics is designed for a first course in undergraduate econometrics. It differs from other textbooks in three main ways. First, it integrates real-world questions and data into the development of the theory. Second, choice of topics reflects modern theory and practice. Third, theory and assumptions that are provided match the applications. Aim of this text is to teach students to become sophisticated consumers of econometrics and to do so at a level of mathematics appropriate for an introductory course.
The Third Edition Update maintains a focus on currency, while building on the philosophy that applications should drive the theory, not the other way around.
 

  • About the Authors
  • Contents
  • Features
  • Downloadable Resources

James H Stock, Harvard University


Mark W. Watson, Princeton University"


 

 

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


Part IV. Regression Analysis of Economic Time Series Data 


14. Introduction to Time Series Regression and Forecasting


15. Estimation of Dynamic Causal Effects 


16. Additional Topics in Time Series Regression


Part V. The Econometric Theory of Regression Analysis 


17. The Theory of Linear Regression with One Regressor


18. The Theory of Multiple Regression


 

 

• Updated treatment of standard errors for panel data regression


• Discussion of when and why missing data can present a problem for regression analysis


• The use of regression discontinuity design as a method for analyzing  quasiexperiments


• Updated discussion of weak instruments


• Discussion of the use and interpretation of control variables integrated into the core development of regression analysis


• Introduction of the "potential outcomes" framework for experimental data


• Additional general interest boxes


• Additional exercises, both pencil-and-paper and empirical


 

 
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