Stephen Donald
Professor — Ph.D., University of British Columbia
Contact
 Email: stephen.g.donald@utexas.edu
 Phone: 5124718907
 Office: BRB 3.102D
 Office Hours: Mon 10anoon, Tues 10a11a
 Campus Mail Code: C3100
Courses
ECO 341K • Introduction To Econometrics
33370 • Fall 2015
Meets TTH 1100am1230pm UTC 3.132
INTRODUCES THE STUDENT TO STANDARD REGRESSION PROCEDURES OF PARAMETER ESTIMATION AND HYPOTHESIS TESTING IN ECONOMICS.
PREREQUISITE: ECONOMICS 420K AND 329 WITH A GRADE OF AT LEAST C IN EACH.
Econometrics is an application of statistical methods to the estimation of economic relationships. Students are expected to have an understanding of both statistics and economic theory. This course reveals how relationships among economic variables are discerned from data. The primary focus of this course is on estimation methodology. If more information is needed contact instructor.
ECO 341K • Introduction To Econometrics
33375 • Fall 2015
Meets TTH 1230pm200pm UTC 4.124
INTRODUCES THE STUDENT TO STANDARD REGRESSION PROCEDURES OF PARAMETER ESTIMATION AND HYPOTHESIS TESTING IN ECONOMICS.
PREREQUISITE: ECONOMICS 420K AND 329 WITH A GRADE OF AT LEAST C IN EACH.
Econometrics is an application of statistical methods to the estimation of economic relationships. Students are expected to have an understanding of both statistics and economic theory. This course reveals how relationships among economic variables are discerned from data. The primary focus of this course is on estimation methodology. If more information is needed contact instructor.
ECO 329 • Economic Statistics
33560 • Spring 2015
Meets TTH 200pm330pm WEL 1.308
METHODS OF STATISTICAL ANALYSIS AND INTERPRETATION OF QUANTITATIVE DATA IN THE FIELD OF ECONOMICS. REQUIRED OF ECONOMICS MAJORS.
PREREQUISITE: ECONOMICS 304K AND 304L WITH A GRADE OF AT LEAST C IN EACH, AND MATHEMATICS 408C AND 408D, OR MATHEMATICS 408K AND 408L, WITH A GRADE OF AT LEAST C IN EACH.
Economics 329 is an introduction to Economic Statistics. The aim of the course is to familiarize students with methods of summarizing collections of measurements (data sets) of economic, political and business phenomena. Of particular concern will be an introduction to elementary probability theory and its use in the interpretation of summary statistics (inference) obtained from statistical data sets. A number of economic, political and business applications will be used to illustrate the methods. If more information is needed contact instructor.
ECO 329 • Economic Statistics
34785 • Spring 2014
Meets TTH 200pm330pm WEL 1.308
METHODS OF STATISTICAL ANALYSIS AND INTERPRETATION OF QUANTITATIVE DATA IN THE FIELD OF ECONOMICS. REQUIRED OF ECONOMICS MAJORS.
PREREQUISITE: ECONOMICS 304K AND 304L WITH A GRADE OF AT LEAST C IN EACH, AND MATHEMATICS 408C AND 408D, OR MATHEMATICS 408K AND 408L, WITH A GRADE OF AT LEAST C IN EACH.
Economics 329 is an introduction to Economic Statistics. The aim of the course is to familiarize students with methods of summarizing collections of measurements (data sets) of economic, political and business phenomena. Of particular concern will be an introduction to elementary probability theory and its use in the interpretation of summary statistics (inference) obtained from statistical data sets. A number of economic, political and business applications will be used to illustrate the methods. If more information is needed contact instructor.
ECO 329 • Economic Statistics
34360 • Spring 2013
Meets TTH 200pm330pm WEL 1.308
METHODS OF STATISTICAL ANALYSIS AND INTERPRETATION OF QUANTITATIVE DATA IN THE FIELD OF ECONOMICS. REQUIRED OF ECONOMICS MAJORS.
PREREQUISITE: ECONOMICS 304K AND 304L WITH A GRADE OF AT LEAST C IN EACH, AND MATHEMATICS 408C AND 408D, OR MATHEMATICS 408K AND 408L, WITH A GRADE OF AT LEAST C IN EACH.
Economics 329 is an introduction to Economic Statistics. The aim of the course is to familiarize students with methods of summarizing collections of measurements (data sets) of economic, political and business phenomena. Of particular concern will be an introduction to elementary probability theory and its use in the interpretation of summary statistics (inference) obtained from statistical data sets. A number of economic, political and business applications will be used to illustrate the methods. If more information is needed contact instructor.
ECO 341K • Introduction To Econometrics
34285 • Spring 2012
Meets TTH 200pm330pm BUR 134
INTRODUCES THE STUDENT TO STANDARD REGRESSION PROCEDURES OF PARAMETER ESTIMATION AND HYPOTHESIS TESTING IN ECONOMICS.
PREREQUISITE: ECONOMICS 420K AND 329 WITH A GRADE OF AT LEAST C IN EACH.
Econometrics is an application of statistical methods to the estimation of economic relationships. Students are expected to have an understanding of both statistics and economic theory. This course reveals how relationships among economic variables are discerned from data. The primary focus of this course is on estimation methodology. If more information is needed contact instructor.
ECO 341K • Introduction To Econometrics
34190 • Fall 2011
Meets TTH 200pm330pm CPE 2.210
INTRODUCES THE STUDENT TO STANDARD REGRESSION PROCEDURES OF PARAMETER ESTIMATION AND HYPOTHESIS TESTING IN ECONOMICS.
PREREQUISITE: ECONOMICS 420K AND 329 WITH A GRADE OF AT LEAST C IN EACH.
Econometrics is an application of statistical methods to the estimation of economic relationships. Students are expected to have an understanding of both statistics and economic theory. This course reveals how relationships among economic variables are discerned from data. The primary focus of this course is on estimation methodology. If more information is needed contact instructor.
ECO 341K • Introduction To Econometrics
34475 • Spring 2011
Meets TTH 200pm330pm ART 1.110
INTRODUCES THE STUDENT TO STANDARD REGRESSION PROCEDURES OF PARAMETER ESTIMATION AND HYPOTHESIS TESTING IN ECONOMICS.
PREREQUISITE: ECONOMICS 420K AND 329 WITH A GRADE OF AT LEAST C IN EACH.
Econometrics is an application of statistical methods to the estimation of economic relationships. Students are expected to have an understanding of both statistics and economic theory. This course reveals how relationships among economic variables are discerned from data. The primary focus of this course is on estimation methodology. If more information is needed contact instructor.
ECO 341K • Introduction To Econometrics
33678 • Spring 2010
Meets TTH 200pm330pm ART 1.110
INTRODUCES THE STUDENT TO STANDARD REGRESSION PROCEDURES OF PARAMETER ESTIMATION AND HYPOTHESIS TESTING IN ECONOMICS.
PREREQUISITE: ECONOMICS 420K AND 329 WITH A GRADE OF AT LEAST C IN EACH.
Econometrics is an application of statistical methods to the estimation of economic relationships. Students are expected to have an understanding of both statistics and economic theory. This course reveals how relationships among economic variables are discerned from data. The primary focus of this course is on estimation methodology. If more information is needed contact instructor.
ECO 392M 2 • Econometrics I
33840 • Spring 2010
Meets TTH 11001230pm BRB 1.118
please download attachment
ECO 341K • Introduction To Econometrics
33850 • Fall 2009
Meets TTH 11001230pm UTC 3.122
ECO 341K  33850 Prof. Stephen Donald
University of Texas Fall 2009
Syllabus: ECO 341K (Introduction to Econometrics)
Meeting time/place: Tues/Thurs 11:00am12:30PM, UTC 3.122
Contact info: donald@eco.utexas.edu
Office hours: Mon 12:30pm, Thurs 12:302pm, or by appointment (BRB 3.126)
Teaching assistant: Ingkyung Kim (BRB 3.154) inkyung987@mail.utexas.edu
Office Hours: 10am12 noon Monday
Summary: This course provides an introduction to econometric methods. The goal is to
provide students with the knowledge to conduct their own empirical research in economics, to
evaluate economic/business policy, to perform forecasting, and to critically read the quantitative
analysis of other researchers. In addition to using the computer as a tool for regression analysis,
the course will focus upon the underlying statistical models so that students understand when
particular methods are likely to be valid (or invalid!).
Textbook: The required textbook for this course is
Introductory Econometrics: A Modern Approach, 4th edition, by Jeffrey Wooldridge (Southwest  Cengage Publishers).
Although we
will jump around in the book throughout the course, we will follow the content in the book rather closely. Most of the sample datasets and homework problems will be taken from the textbook. The 3rd edition is also fine (and probably a lot cheaper if you can find it second hand). There are a few more problems in the 4th edition but it should not be a problem.
Prerequisites: Economics 420K (Microeconomic Theory) and 329 (Economic Statistics) with a
grade of at least C in each. You should be familiar with most, if not all, of the material in Appendices A ("Basic Mathematical Tools"), B ("Fundamentals of Probability"), and C ("Fundamentals of Mathematical Statistics") of the textbook.
Software: Students are required to use the statistical package STATA in this course. It is very
easy to learn. Class examples will be illustrated using STATA, and students will be expected to use STATA for the empirical exercises on their problem sets. There are a few options for accessing STATA: (i) establish an Austin Disk Services account (if you haven't already) for a small annual fee and access STATA through the Windows Terminal Services (http://www.utexas.edu/its/windows/), (ii) use the computers in the BUR 120 or 124 labs, or (iii) purchase your own oneyear li ense ($95) for STATA/IC 10 (not Small STATA) through http://www.stata.com/order/new/edu/gradplans/gpdirect.html.
Website : All lecture notes, example sheets, homework assignments/solutions and STATA
datasets will be posted on the course Blackboard site.
Lecture Notes: The lecture notes will be made available in Blackboard prior to the lectures.
Also empirical examples will be used extensively. These will be available in both pdf and word formats.
Grades : Course grades will be determined by the following weights
Homework: 20%
Extra Credit: 2.5%
Two inclass exams: 20% each (dates Oct 1 and Oct 29)
Final exam: 40% (date TBA)
I will be using the new plus/minus grading system. I do not take attendance.
Homework: Homework assignments will be graded on completeness not correctness. There
will be weekly assignments due at the beginning of Tuesday's class (except for the weeks of the inclass exams). Each will be worth 2 points. Late assignments will not be accepted. (If you can not make it to class, have a classmate bring your assignment or email it to me before class begins.) You may work with one other person on the homework, but you must turn in your own answers (and indicate with whom you worked). Include all necessary computer output with your assignment. You will be allowed to drop your two lowest scores on the assignments.
Exams : All exams will be closed book. I will provide a common "formula sheet" for these
exams to minimize the amount of memorization required. There will be no makeup exams for the inclass exams; if you have a valid medical excuse (and a doctor's note), I will put more weight on the final.
Disabilities: Students with disabilities may request appropriate academic accommodations from
the Division of Diversity and Community Engagement, Services for Students with Disabilities, 4716259.
Course outline (topics near end to be covered as time permits; "W"=Wooldridge):
1. Introduction (W 1)
a. What is econometrics?
b. Types of economic data
c. Causality vs. correlation
2. The simple regression model (W 2.12.5)
a. Model and assumptions
b. Ordinary least squares (OLS) estimator
c. Goodnessoffit and Rsquared
d. Nonlinear (logarithmic) transformations
e. Properties of OLS
3. The multiple regression model (W 3)
a. How do the simple regression results extend?
b. Omitted variables bias
c. Multicollinearity
d. GaussMarkov theorem: efficiency of OLS
4. Statistical inference ("finite sample") for OLS (W 4, 5)
a. Confidence intervals
b. Single parameter tests: "t test"
c. Twosided versus onesided test
d. pvalues
e. Multiple restriction tests: "F test"
f. Asymptotic ("large sample") theory for OLS (W 5.15.2, skip the LM statistic in 5.2)
5. Additional issues in regression analysis
a. Prediction (6.4)
b. Binary variables (7)
c. Heteroskedasticity (W 8.18.3, skip LM test in 8.2, skip White test in 8.3)
d. Measurement error (W 9.3)
e. Outliers (W 9.5, starting on p. 325)
6. Time series analysis (W 10, 11.111.3)
a. Types of models
b. Trends and seasonality
c. Serial correlation  AR(1) model, "random walk"
7. Panel data (W 13, 14.1, 14.3)
a. Pooled cross sections
b. Fixed effects model
8. Binarychoice models (W 17.1)
9. Instrumental variables (W 15.115.3)
a. Endogeneity
b. Twostage least squares estimation
Class Schedule
Date Lect. Ch. Class
Data Files (all .dta
STATA)
Notes
File
Examples
File
27Aug09 1 1 Introduction  Syllabus ch1
1Sep09 2 1 Data/CorrelationCausality/SLRM wage1, caschool ch1 ch1examp
3Sep09 3 2 SLRM  Assumptions, OLS ceosal1,wage1 ch2 ch2examp 8Sep09 4 2
SLRM  Interpretation, Transformation, Fit stocks, wage1 ch2 ch2examp
10Sep09 5 2 SLRM  Properties of OLS caschool, cars93 ch2 ch2examp
15Sep09 6 3 MLRM  Model, Assumptions OLS wage1, cars93 ch3 ch3examp
17Sep09 7 3 MLRM  Interpretation, Fit hprice1, cashool, cars93 ch3 ch3examp
22Sep09 8 3 MLRM  Exp. Value, Omit. Var., Collin wage1, cashool, cars93 ch3 ch3examp
24Sep09 9 3 MLRM  Variance, Gauss Markov cars93 ch3 ch3examp
29Sep09 Review and CatchUp
1Oct09 Midterm Exam 1
6Oct09 10 4 Inference  Small Sample wage1, hprice2, stocks, cars93 ch45 ch45examp
8Oct09 11 4 Inference  Small Sample stocks, bwght ch45 ch45examp
13Oct09 12 5 Inference  Large Sample bwght ch45 ch45examp
15Oct09 13 6 Further Issues  Prediction hprice1 ch67 ch67examp
20Oct09 14 6 Further Issues  Functional Form hprice2, wage1, hprice1 ch67 ch67examp
22Oct09 15 7 Further Issues  Qualitative Variables wage1 ch67 ch67examp
27Oct09 Review and CatchUp
29Oct09 Midterm Exam 2
3Nov09 16 6 Further Issues Heterosked. Outliers wage1, infmrt, fla2000 ch67 ch67examp
5Nov09 17 10 Time Series Regression hseinv, fertil3 ch1011 ch1011examp
10Nov09 18 11 Time Series Regression dowjones, austemp ch1011 ch1011examp
12Nov09 19 13 Pooled Cross Section fertil1, kielmc ch1314 ch1314examp
17Nov09 20 14 Panel Data Regression crime ch1314 ch1314examp
19Nov09 21 7\17 Binary Choice titanic ch717 ch717examp
24Nov09 Review and CatchUp
26Nov09 Thanksgiving
1Dec09 22 15 IV wage2, fultonfish, card ch15 ch15examp
3Dec09 Review and CatchUp
Research
Rank Test Program
In Cragg and Donald (1997) Journal of Econometrics we considered a Minimum Chi Squared type test for the rank of an estimated matrix where the estimator is asymptotically Normal with general covariance matrix. The program that implements the test in Gauss is below.
If you use the program please cite the above mentioned paper.
 Files Attached
 RANKM.GAU
Empirical Welfare Analysis
The NSF (SES0196372) provided research support for the papers below. Gauss programs for testing stochastic dominance up to order 3 also are available below.
 Files Attached
 "Consistent Tests for Stochastic Dominance" (Econometrica 2003)
 Graphs  "Consistent Tests for Stochastic Dominance"
 KS  Simulated Test (SDKStest.gau)
 KS  Bootstrap Test (SDKSBtst.gau)
 Wald Test (SDWald.gau)
 Maximal T  Test (SDMaxT.gau)
 Maximal T  Test, Anderson Calculations (SDAndrsn.gau)
 "A Consistent Nonparametric Test for Lorenz Dominance" (Barrett and Donald, 1999)
 "Statistical Inference with Generalized Gini Indices" (Barrett & Donald, 2000)
Other Papers
"Empirical Likelihood Estimation and Consistent Tests with Conditional Moment Restrictions" (Donald and Imbens, 2001)  Published in the Journal of Econometrics, 2003.
"Testing Overidentifyng Restrictions in Unidentified Models" (Cragg and Donald, 1996)  Appeared in the UBC Discussion Paper Series 96/20.
Sydney
Some Scenes From Sydney (Australia of course)
I grew up somewhere way past that tower on the left.
Here is another view.
As an undergrad I majored in Econometrics at Sydney University  this is a nice part of campus.
An aerial shot of the Olympic Stadium and other venues.
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