Final exam on Tues Apr 25, 3:30-6:15pm in GPA 113 (Golden Panther Arena).

New lecture room: GPA 113 (Golden Panther Arena). We will meet for lectures there starting the week of Jan 24.
 

Syllabus
Required textbook -- Econometric Analysis, by William Greene, 5th Edition.

Sample Regression Program

Data for Homework 1

Data for Homework 2

Data for Homework 3
 

Lecture	Topics to be covered
1	GAUSS program
2	Specification of the GLSM
3	GAUSS lab session
4	GAUSS lab session
5	Matrix algebra
6	Estimation of GLSM by Least Squares (LS)
7	LS as projections in vector spaces; projection matrix; Frisch-Waugh theorem
8	Estimating subset of parameter vector; Orthogonal vectors; orthogonal regression
9	Goodness-of-fit measures; R-squared; R-bar-squared; AIC; SBC
10	Estimating sigma-squared; Unbiasedness of  beta-hat; variance of beta-hat; Gauss-Markov theorem
11	Multicollinearity; Variable specification errors - underparameterization and overparameterization
12	Exact tests under normality
13	The t-test
14	The F-test
15	Review
16	Midterm
17	Asymptotic theory - modes of convergence; convergence in probability; convergence in distribution
18	Slutsky theorem; continuous mapping theorem; weak law of large numbers; central limit theorem (CLT)
19	A useful CLT; asymptotic properties - unbiasedness, efficiency, and consistency; asymptotic distribution of beta-hat and sigma-hat-squared
20	Asymptotic t- and F- tests
21	Correlation between regressor and error - consequences on OLS; forward and reverse regressions; consistent bounds by OLS
22	Method of moments; IV estimation; consistency of IV estimator
23	Where do instruments come from? What to do when we have more instruments than we need?
24	Properties of IV estimator; testing for correlation between regressor and error; Generalized Least Squares (GLS)
25	Covariance matrix under pure heteroskedasticity; weighted least squares (WLS); feasible and infeasible GLS
26	Distribution of feasible and infeasible GLS; test statistics
27
28
29
30