ECO 341K -- 33850 Prof. Stephen Donald
University of Texas Fall 2009
Syllabus: ECO 341K (Introduction to Econometrics)
Meeting time/place: Tues/Thurs 11:00am-12:30PM, UTC 3.122
Contact info: donald@eco.utexas.edu
Office hours: Mon 1-2:30pm, Thurs 12:30-2pm, or by appointment (BRB 3.126)
Teaching assistant: Ingkyung Kim (BRB 3.154) inkyung987@mail.utexas.edu
Office Hours: 10am-12 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 one-year li ense ($95) for STATA/IC 10 (not Small STATA) through http://www.stata.com/order/new/edu/gradplans/gp-direct.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 in-class 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 in-class 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 e-mail 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 make-up exams for the in-class 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, 471-6259.
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 2.1-2.5)
a. Model and assumptions
b. Ordinary least squares (OLS) estimator
c. Goodness-of-fit and R-squared
d. Non-linear (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. Gauss-Markov theorem: efficiency of OLS
4. Statistical inference ("finite sample") for OLS (W 4, 5)
a. Confidence intervals
b. Single parameter tests: "t test"
c. Two-sided versus one-sided test
d. p-values
e. Multiple restriction tests: "F test"
f. Asymptotic ("large sample") theory for OLS (W 5.1-5.2, skip the LM statistic in 5.2)
5. Additional issues in regression analysis
a. Prediction (6.4)
b. Binary variables (7)
c. Heteroskedasticity (W 8.1-8.3, 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, 11.1-11.3)
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. Binary-choice models (W 17.1)
9. Instrumental variables (W 15.1-15.3)
a. Endogeneity
b. Two-stage least squares estimation
Class Schedule
Date Lect. Ch. Class
Data Files (all .dta
STATA)
Notes
File
Examples
File
27-Aug-09 1 1 Introduction -- Syllabus ch1
1-Sep-09 2 1 Data/Correlation-Causality/SLRM wage1, caschool ch1 ch1examp
3-Sep-09 3 2 SLRM - Assumptions, OLS ceosal1,wage1 ch2 ch2examp 8-Sep-09 4 2
SLRM -- Interpretation, Transformation, Fit stocks, wage1 ch2 ch2examp
10-Sep-09 5 2 SLRM -- Properties of OLS caschool, cars93 ch2 ch2examp
15-Sep-09 6 3 MLRM -- Model, Assumptions OLS wage1, cars93 ch3 ch3examp
17-Sep-09 7 3 MLRM -- Interpretation, Fit hprice1, cashool, cars93 ch3 ch3examp
22-Sep-09 8 3 MLRM -- Exp. Value, Omit. Var., Collin wage1, cashool, cars93 ch3 ch3examp
24-Sep-09 9 3 MLRM -- Variance, Gauss Markov cars93 ch3 ch3examp
29-Sep-09 Review and Catch-Up
1-Oct-09 Midterm Exam 1
6-Oct-09 10 4 Inference -- Small Sample wage1, hprice2, stocks, cars93 ch4-5 ch4-5examp
8-Oct-09 11 4 Inference -- Small Sample stocks, bwght ch4-5 ch4-5examp
13-Oct-09 12 5 Inference -- Large Sample bwght ch4-5 ch4-5examp
15-Oct-09 13 6 Further Issues -- Prediction hprice1 ch6-7 ch6-7examp
20-Oct-09 14 6 Further Issues -- Functional Form hprice2, wage1, hprice1 ch6-7 ch6-7examp
22-Oct-09 15 7 Further Issues -- Qualitative Variables wage1 ch6-7 ch6-7examp
27-Oct-09 Review and Catch-Up
29-Oct-09 Midterm Exam 2
3-Nov-09 16 6 Further Issues --Heterosked. Outliers wage1, infmrt, fla2000 ch6-7 ch6-7examp
5-Nov-09 17 10 Time Series Regression hseinv, fertil3 ch10-11 ch10-11examp
10-Nov-09 18 11 Time Series Regression dowjones, austemp ch10-11 ch10-11examp
12-Nov-09 19 13 Pooled Cross Section fertil1, kielmc ch13-14 ch13-14examp
17-Nov-09 20 14 Panel Data Regression crime ch13-14 ch13-14examp
19-Nov-09 21 7\17 Binary Choice titanic ch7-17 ch7-17examp
24-Nov-09 Review and Catch-Up
26-Nov-09 Thanksgiving
1-Dec-09 22 15 IV wage2, fultonfish, card ch15 ch15examp
3-Dec-09 Review and Catch-Up
