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Introduction to Econometrics

Introduction to Econometrics

Author(s):
  • James H. Stock
  • Mark W. Watson
    • Author: James H. Stock
    • ISBN:9789352863501
    • 10 Digit ISBN:935286350X
    • Price:Rs. 1000.00
    • Pages:840
    • Imprint:Pearson Education
    • Binding:Paperback
    • Status:Available


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    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.

     

    Table of Content

    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
     

    Salient Features

    • 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