ECO 745-01: Advanced Econometric Theory
University of North Carolina Greensboro
Fall 2015
Contact Information
Instructor:
Office:
Phone:
Email:
Martijn van Hasselt
Bryan 446
(336) 334-4872
mnvanhas@uncg.edu
Lectures:
Office hours:
Monday, Wednesday, 2:00PM – 3:15PM in Bryan 456
by appointment
Graduate Assistant:
Office:
Email:
Maozhao Zheng
Bryan 469
m_zheng@uncg.edu
Course Description
This course is the first in a two-course sequence on econometric theory for economics Ph.D.
students. It covers least squares estimation and, more generally, method-of-moments estimation
and testing from a large sample perspective. Tools from asymptotic analysis will be used to study
estimators and test statistics. Topics include the linear regression model, instrumental variables,
multiple-equation estimation, panel data and serial correlation.
Learning Objectives
On completion of this course, students will

have built a foundation in econometric theory that includes knowledge of a number of
models and estimators that are commonly used in microeconometric empirical practice;

be able to apply asymptotic tools needed to derive the large sample properties of common
estimators and test statistics;

understand how modeling assumptions (about identification, error structure, etc.) affect
the behavior of certain estimators.
Course Grade
The course grade will be based on three components:

Problem sets (30%)

Midterm exam (30%)

Final exam (40%)
Problem sets will be handed out periodically throughout the semester. For some of them you will
use Stata. The midterm exam will be a take-home exam. The final exam is cumulative and will
cover the entire semester.
Course Materials
The following text is required for this course:

Hayashi, F. (2000). Econometrics. Princeton University Press
Additional readings (e.g., articles, book chapters) may be used. If this happens, these will be
provided to you via Canvas. You may also find the following references helpful as supplemental
reading (these are not required).

Casella, G. and Berger, R.L. (2001). Statistical Inference. Duxbury. [This text is a good
source for mathematical statistics.]

Greene, W.H. (2011). Econometric Analysis. Prentice Hall. [The book is currently in its
7th edition, but older editions can be used as well.]

Wooldridge, J.M. (2010). Econometric Analysis of Cross Section and Panel Data. MIT
Press. [This is a very popular graduate-level textbook.]

Cameron, A.C. and Trivedi, P. K. (2005). Microeconometrics: Methods & Applications.
Cambridge University Press.

Goldberger, A.S. (1991). A Course in Econometrics. Harvard University Press.
Tentative Schedule
Week
(1) Aug. 17, 19
Topics
Review of probability and matrix algebra
The linear regression model
 Assumptions
 The algebra of ordinary least squares (OLS)
(2) Aug. 24, 26
 Finite sample properties of OLS
 Hypothesis testing under normality
(3) Aug. 31, Sep. 2  OLS and maximum likelihood estimation
 Generalized least squares (GLS)
(4) Sep. 9
No class on September 7 (Labor Day)
Tools for asymptotic analysis
 Convergence in probability and distribution
 Laws of large numbers and central limit theorems
(5) Sep. 14, 16
Time series concepts
 Ergodic stationarity
 Martingale differences
OLS
 Large sample properties
 Variance estimation
Readings
H: 1.1, 1.2
H: 1.3, 1.4
H: 1.5, 1.6
H: 2.1
H: 2.2, 2.3, 2.5
Week
(6) Sep. 21, 23
Topics
OLS
 Hypothesis testing
 Homoscedasticity
 Linear prediction
(7) Sep. 28, 30
 Endogeneity and bias of OLS
 Instrumental variables
 Generalized method of moments (GMM)
(8) Oct. 5, 7
GMM
 Large sample properties
 Testing over-identifying restrictions
(9) Oct. 14
No class on October 12 (Fall Break)
GMM
 Two-stage least squares (2SLS)
(10) Oct. 19, 21
GMM in multiple-equation models
 Estimator and large sample properties
 Singe-equation versus multiple-equation estimators
(11) Oct. 26, 28
Special cases of multiple-equation GMM
 Three-stage least squares (3SLS)
 Seemingly unrelated regressions (SUR)
 Common coefficients
 Pooled OLS
(12) Nov. 2, 4
Panel data models
 Individual heterogeneity
 Fixed effects estimation
(13) Nov. 9, 11
Panel data models
 Random effects estimation
 The Hausman test
 Unbalanced panels
(14) Nov. 16, 18
Serial correlation
 Lag polynomials
 Moving average processes
 Autoregressive moving average (ARMA) models
 Vector autoregression (VAR)
(15) Nov. 23
No class on November 25 (Thanksgiving Break)
Serial correlation
 Estimation of ARMA models
(16) Nov. 30
Serial correlation
 Asymptotic tools
 Serial correlation in GMM
Final Exam: Monday, December 7, 3:30PM – 6:30PM
Readings
H: 2.4, 2.6, 2.9
H: 3.1-3.4
H: 3.5-3.7
H: 3.8-3.9
H: 4.1-4.4
H: 4.5-4.6
H: 5.1-5.2
H: 5.2-5.3
H: 2.10, 6.1-6.3
H: 6.4
H: 6.5-6.6