Econ 122 Introduction to Econometrics


Spring 2013

 

Course Description: This course will introduce statistical analysis of linear models, as applied to economic data. Though much of the course will be devoted to derivation of econometric theory, applications of this theory to particular problems in the analysis of economic data will also be discussed.

 

Prerequisites: ECON-121 and MATH-035. The first few lectures will review the pertinent material from statistics; students should be comfortable with these basic concepts of probability theory and statistical inference, and, more generally, with mathematical derivations.

Course Requirements: The grade in this course will be based on problem sets (worth 20% of the grade), a midterm exam (worth 30% of the grade), and a final exam (50%). There will be no makeup exams - scheduling conflicts should be discussed with me at least a week prior to the exam. The course will also make use of an econometrics package called GRETL. Details on how to access the package will be given in the first class.

Required Text:  Introduction to Econometrics, by James Stock and Mark Watson, Addison Wesley. Website   www.aw.com/stock_watson

Office Hours: TBA (x7-1570)

Email: evansm1@georgetown.edu

Class: TBA  

TA: TBA

Lecture Notes and Problem Sets are available on the Blackboard Website

 

Course Outline and Readings

  1. Review of Probability and Statistical Inference
    1. Introduction:  Economic Questions and Data
    2. Review of probability: Random variables, Expectations
    3. Review of Statistics:Estimation, Hypothesis Testing

          (S&W Chapters 1, 2 & 3) 

  1. Regression Analysis
    1. Linear Regression with One Regressor
    2. Linear Regression with Multiple Regressors 
    3. Nonlinear Regression
    4. Assessing Regression Results
           
                (S&W Chapters 4 - 7, 15 and 16)

  1.     Advanced Topics (As time permits)
    1. Instrumental Variables (S&W Chapter 10)
    2. Binary Models  (S&W Chapter 9)
    3. Panel Data  (S&W Chapter 8)
    4. Time Series  (S&W Chapter 12).