Dr. Philip Shaw
Dealy Hall, East 522
Office Hours: Monday 4:00pm-5:00pm & Thursday 4:00pm-5:00pm on Zoom.
class will begin with an exploration of the properties required to
obtain causality in econometrics. We will focus largely on
the theoretical properties of conditional expectations operators and
basic asymptotic theory applied to ordinary least squares (OLS),
two-stage least squares (2SLS), and nonlinear methods such as discrete
The grading for the class breaks down as follows:
Final Exam (40%)
Problem Sets (20%)
Wooldridge, J., 2010. Econometric Analysis of Cross Section and Panel Data, MIT Press, Edition 2.
1. Causal Relationships and Ceteris Paribus Analysis
2. Conditional Expectations and Related Concepts in Econometrics
3. Basic Asymptotic Theory
II. Linear Models
4. Single-Equation Linear Model and Ordinary Least Squares Estimation
5. Instrumental Variables Estimation of Single-Equation Linear Models
6. Additional Single-Equation Topics
7. System Estimation of Instrumental Variables and Simultaneous Equations Models
III. Nonlinear Models and Related Topics
8. Binary Response Models
Where to get R: http://cran.wustl.edu/
Simple Commands in R
R-code Posted Below
Data Sets and Descriptions:
DOWNLOAD ENTIRE DATA SET BELOW:
INDIVIDUAL DATA SETS:
Additional Readings Posted BelowInstrumental Variables Estimation Additional Readings
Staiger, Douglas and Stock, James H., 1997. "Instrumental Variables Regression with Weak Instruments"., Econometrica, Vol. 65 (3), 557-586. Download Here!
Moreira, Marcelo., 2003. "A Conditional Likelihood Ratio Test for Structural Models"., Econometrica, Vol. 71 (4), 1027-1048. Download Here!
Lewbel, Arthur., 2012. "Using Heteroscedasticity to Indentify and Estimate Mismeasured and Endogenous Regressor Models"., Journal of Business & Economic Statistics, Vol. 30 (1), 67-80. Download Here!