Instructor: Helle Bunzel
Office: 373 Heady Hall
Phone: 4-6163
e-mail: hbunzel@econ.iastate.edu
Office Hours: MW 11-12 and 3-3:30
TA: Dong Yan
TA Office: 71 Heady Hall
TA phone: 294-8717
TA e-mail: yan@iastate.edu
TA office hours: MW 1:00-1:30pm
ANNOUNCEMENTS
| Class Time | M, W 9:00 - 10:45am (Heady 162) |
| Section Time | Friday 1-2pm, Heady 274 |
| Problem Sets due | Wednesdays, beginning of class |
| Midterm 1 | Wednesday, October 3. |
| Midterm 2 | Wednesday, November 7. |
| Final Exam | Friday, Dec. 14 3-5 p.m. 2319 Food Science |
Course Administration
Course Objective: This is a first course
in econometrics. The purpose of the course is to provide you with solid
foundations that will provide you with the theory that will eventually
enable you to do solid empirical work. In addition you should be
able to update your knowledge as needed in the years to come.
Readings: The required text is Econometric
Analysis by William H. Greene. All page/graph references and problem numbers
in the course will refer to the fourth edition. Two copies of the book
will be on reserve in the reading room. For those of you who already have
older editions of this book, it should be possible to use those and simply
look up specific references to the fourth edition in the copies of the
book found in the reading room.
Additional sources: Two other books which might be useful are:
Estimation and Inference in Econometrics
by Davidson and MacKinnon
Econometric Methods by Jack
Johnston and John DiNardo.
Lectures: The lectures will be held in Heady Hall 162 Monday and Wednesday 9-11am. Attendance is strongly recommended. It is your responsibility to find out what was covered if you do miss a lecture.
Recitation Session: There will be a weekly
section held by the TA. The purpose of the section is to go over the problem
sets and to review some materials.
Assignments: Problem sets will be assigned approximately once a week and they will be graded. The problem set is due at the beginning of class every Wednesday. A problem set which is not turned in on time will not be accepted and a score of 0 will be recorded. At the end of the semester the problem set with the lowest score will not be counted towards your grade.
Exams: Two midterms and one final
exam will be given. If you have a conflict with
the time of the final, please let me know a soon as possible
and absolutely no later than
December 10. All exams are open book exams.
The midterms will be given during class time and will
have a length of 100 minutes.
The midterms will be incremental, but the final will
be cumulative.
Grading: The problem sets will count for
20% of the course. Each midterm carries a
weight of 20% and the final carries a weight of 40%.
No make-up midterms will be
given. If you miss one midterm with a valid reason, the
remaining midterm will receive
a weight of 30% and the final a weight of 50%. The professor
will determine whether
a reason is valid. You MUST attend at least one midterm.
Academic Integrity: All violations of academic
integrity are taken very seriously, and will be reported to the appropriate
authority.
Contact information: If you need to get
in touch with me, send me an e-mail.
The address is hbunzel@econ.iastate.edu. You are also
welcome to stop by any
time.
Syllabus and Reading List:
We are done with the topics in red.
Greene Chapter 3, all subsections.
Statistical Inference:
A review of the basics of testing
and asymptotic theory.
Greene Chapter 4.1-4.4 + pages 123-127.
The Linear Regression Model:
An introduction to the classical
linear regression model.
Greene 6.1-6.3
Ordinary Least Squares:
The standard estimation method
and its properties.
Basic method:
Greene 6.4 (excluding 6.4.4)
Goodness
of fit: Greene 6.5
Statistical
Properties: Greene 6.6
Diagnostics:
Examination of various problems
which can affect the validity of the ordinary least squares method and
the methods used to diagnose them.
Multicollinearity:
Greene 6.7
Missing
observations: Greene 6.8
Influential
data points: Greene 6.9
Testing One Linear Restriction:
A simple test of a single restriction
on the model parameters.
Greene 7.2.3
Testing Several Linear Restrictions:
How to test several linear restrictions
at the same time.
Greene 7.2.4
Asymptotic Properties of the
Ordinary Least Squares Estimator:
Examines the properties (distribution
and limit) of the estimator as the number of observations approach infinity.
Greene 9.1, 9.2
and 9.3
Imposing Restrictions on the
Ordinary Least Squares Estimator:
Examination of the estimator when
restrictions are imposed a-priori and use of the properties to conduct
tests.
Greene 7.3, 7.4 and 7.5
Tests of Structural Change:
An examination of test to examine
whether the model is correct across the whole data set.
Greene 7.7, 7.8
Choosing Between Non-Nested Models:
Selecting one of two models, when
one cannot be written as the other with linear restrictions imposed.
Greene 7.10
Prediction:
How to use the standard methods
to predict future outcomes.
Greene 7.11
Instrumental Variable Estimation:
Description of the IV method and
the Hausman test for exogeneity of the X-variables.
Greene 9.5.1,
9.5.4
Maximum Likelihood and the Three
Classical Test Statistics:
Introduction to asymptotic efficiency
and Maximum Likelihood as well as the three classical test: The Likelihood
Ratio, Lagrange Multiplier and Wald tests.
Greene 9.6.1,
9.6.2 and 9.6.3 (NOTE: 9.6.3 is full of errors!)
Non-linear Least Squares Estimation:
A quick introduction the the NLS
estimation method used when the model cannot be rewritten as a linear model.
Greene 10.2,
10.2.1, 10.2.3
Estimation When the Errors Have
a General Variance Structure:
Introduction to Generalized Least
Squares, heteroscedasticity and autocorrelation.
GLS: Greene
11.1, 11.3.1, 11.4.1
OLS with
this error structure: Greene 11.2.1
Heteroscedasticity:
Greene 12.1, 12.2, 12.3
Serial
correlation: Greene 13.6.1, 13.5
Maximum
Likelihood: Greene 11.3.2