...

ECON 7828-001 Econometrics

by user

on
Category: Documents
1

views

Report

Comments

Transcript

ECON 7828-001 Econometrics
Economics 7828 - Econometrics
Spring 2013
Robert McNown
Office: Economics 109
Telephone: 303-492-8295
Office Hours: M 9-11
R 2-3:30
Email: [email protected]
Economics 7828 is a course in intermediate econometrics for PhD students.
Building upon the statistical foundations presented in 7818, this course covers both
theoretical and applied aspects of econometrics. Economics 7818 is the prerequisite for
this course, which requires a solid background in mathematical statistics and matrix
algebra. Our text also presents a brief summary of the essentials of matrix algebra and
multivariate statistics in appendices A and B.
You will be actively working with computers in this course. Computer exercises
and instruction are provided for both EViews and State econometrics packages that are
available on our PC network. These exercises are designed to illustrate the use of an
econometric software package and to develop skills in the application of econometric
tests and procedures to economic data. Instructions and data sets for these exercises
will be accessible from the Desire2Learn site for our class. If you prefer to use a
different econometric software package, such as R, this is fine, but you will have to
learn this alternative software on your own.
Homework problems will be assigned periodically, and completion of these is
essential to learning econometrics and, incidentally, doing well on the exams and the
project. You are encouraged to form teams of two for working homework problems,
computer exercises, and the applied econometrics project described below. Each team
needs to submit only one copy of any assignment.
Your grade in the course will be based on a midterm exam and a final exam,
each counting towards 30% of your grade, and homework (including computer
exercises) and an applied regression project, each for 20% of your grade.
Applied Econometrics Projects
The project involves the application of econometric analysis to the estimation and
testing of a model of your team’s choice. Your written paper will resemble empirical
papers in social science or business research, but with more details on the econometric
analysis than you might find in published articles. Ideas for topics may be found in The
Review of Economics and Statistics, Applied Economics, and other applied economics
journals. You may also get some ideas from other economics courses, and from
examples presented in the text, the EViews/Stata exercises, or in lectures.
Although the topic choice is fairly open-ended, I want to make sure that every
team finds an appropriate topic and does so long before the end-of-term rush. You are
therefore required to submit a brief written proposal identifying the topic you will
investigate, sketching a tentative model for estimation, describing hypotheses to be
tested and questions to be addressed, and identifying the data sources and some
background literature relevant to your project. This can be done in two or three pages.
This proposal is due on Thursday, February 21. You are invited to discuss your ideas
with me at any time during the development of your project. One purpose of this
proposal is to have you identify your data sources early in the term so that you will not
be caught later in the semester with a project that is not feasible for lack of data.
Once we have agreed on a project you should collect the necessary data and
proceed with the estimation. In estimating your model there may be several variants you
will try (alternative functional forms, differing variable definitions, alternative lag
structures, alternative estimation techniques etc.). You will likely encounter various
econometric problems or be involved with advanced estimation procedures (panel data
methods, logit-probit models, instrumental variables estimation, etc.). An important part
of your assignment is dealing with econometric problems (autocorrelation,
heteroscedasticity, multicollinearity, etc.) or implementing advanced estimation
procedures. In some cases we will not have covered your econometric method before
you need to proceed with the estimation, so you will need to read about this topic on
your own. The evaluation of your project will reflect, in part, your skill in handling these
econometric problems, the sophistication of the analysis, and your interpretation and
testing of variants of your basic model.
When you have completed your estimation, you should prepare your final report
following the format of empirical articles in economics journals. Typically these papers
include the following:
1. Introductory overview of the research question; statement of objectives.
2. Presentation of theory and review of relevant theoretical literature.
3. Discussion of previous empirical work in the area; critique and explanation of
why your approach is vastly superior, or at least different.
4. Specification of your model(s) to be estimated; variable definitions and
description of data sources.
5. Presentation of results: estimated equations and summary statistics; results of
tests of econometric problems and description of corrective actions taken; results of
statistical tests of hypotheses; comparison with other studies.
6. Discussion and conclusion; elaborate on the implications of your results for
theory and policy; draw as much substantive content as possible from interpretations of
your estimates and tests of hypothesis; present suggestions for further research (now
that I have done all this work, this is how I would do it right).
7. Bibliography; list your data sources and any literature that you have cited in
the paper. Keep in mind that any text or mathematical derivations that have been copied
from other sources must be identified with quotation marks and given appropriate
references. Quoted text should be kept to a minimum; most of the writing should be
your own. When you have relied on other works for ideas (e.g., models, explanations,
interpretations, etc.) these sources must be given credit also. Our Department now
requires submission of a cover sheet for the paper that attests that the research and the
writing are your own, and I will make sure that you have access to this cover sheet.
Your final paper is due on Thursday, April 25. Include with your written paper the
computer printouts of your most important results, with some guide to the output in your
text. Late papers will be penalized by 10 percentage points if I receive it before I must
post grades, and by 20 percentage points if it is any later.
2
Readings and Topics
Text: Verbeek, Marno (2008) A Guide to Modern Econometrics, 3rd edition. Wiley.
0. Introduction: some econometrics controversies [Chapter 1; Readings: “Housing
Experiment” and “Cause and Defect”]
I. Classical Linear Regression: least squares estimation, properties of estimators, and
tests of simple hypotheses and general linear restrictions; alternative functional forms;
dummy variables. [Chapters 2 and 3; Reading: Angrist and Pischke, “The Credibility
Revolution in Empirical Econometrics”]. In class we will develop the econometric theory
of Chapter 2 in great detail, while you will cover on your own the more applied material
of Chapter 3.
II. Generalized Linear Model: heteroscedasticity and autocorrelation [Chapter 4;
Reading: Granger, Hyung, & Jeon, “Spurious regressions with stationary series”].
Analyze possible departures from the classical regression model using the DITS
framework: (1) Definition of the problem; (2) Implications for OLS properties; (3) Testing
for its occurrence; (4) Solution or treatment.
Midterm Examination - March 5
III. Endogeneity, Identification, and Instrumental Variables Estimation [Chapter 5;
Reading: Angrist & Krueger, “Instrumental Variables and the Search
for Identification”]. In modern econometrics, endogeneity seems to be hiding in every
model. We will follow a heuristic approach to the question of identification, with a more
formal presentation of IV estimation. Angrist and Krueger present a more modern
approach to identification and the uses of instrumental variables estimation.
IV. Panel data methods and seemingly unrelated regressions [Chapter 10]. Many of you
may be working with panel or longitudinal data in your projects, and Chapter 10
presents more advanced methods than we will be able to cover in class.
V. Time Series Econometrics: stationarity, unit roots; cointegration and vector
autoregressions [Chapter 8, pages 269-293; Chapter 9, pages 323-338; Reading:
Granger & Newbold, “Spurious Regressions in Econometrics”]. This is probably my only
chance to introduce you to the methods that are central to my own research, and the
only place you will find these models in our graduate curriculum these days.
VI. Limited Dependent Variables [Chapter 7]. I doubt we will have any time to cover
these topics, and I will not hold you responsible for this material. I list this chapter for
completeness and because some of you may pursue a project using these methods.
Final Examination - May 7 (Tuesday 1:30-4:00)
Syllabus Addendum
(1)
If you qualify for accommodations because of a disability, please submit to me a letter
from Disability Services before the fourth week of classes so that your needs be addressed.
Disability Services determines accommodations based on documented disabilities. Contact:
303-492-8671, Willard 322, and www.Colorado.EDU/disabilityservices
(2)
Campus policy regarding religious observances requires that faculty make every effort
to deal reasonably and fairly with all students who, because of religious obligations, have
conflicts with scheduled exams, assignments or required attendance. In this class, you must
inform the instructor of religious obligations two weeks in advance of a conflict. See
http://www.colorado.edu/policies/fac_relig.html for details on university policies.
(3)
Students and faculty each have responsibility for maintaining an appropriate learning
environment. Those who fail to adhere to such behavioral standards may be subject to
discipline. Professional courtesy and sensitivity are especially important with respect to
individuals and topics dealing with differences of race, culture, religion, politics, sexual
orientation, gender, gender variance, and nationalities. Class rosters are provided to the
instructor with the student's legal name. I will gladly honor your request to address you by an
alternate name or gender pronoun. Please advise me of this preference early in the semester so
that I may make appropriate changes to my records. See policies at
http://www.colorado.edu/policies/classbehavior.html and at
http://www.colorado.edu/studentaffairs/judicialaffairs/code.html#student_code
(4)
The University of Colorado at Boulder policy on Discrimination and Harassment, the
University of Colorado policy on Sexual Harassment and the University of Colorado policy on
Amorous Relationships apply to all students, staff and faculty. Any student, staff or faculty
member who believes s/he has been the subject of discrimination or harassment based upon
race, color, national origin, sex, age, disability, religion, sexual orientation, or veteran status
should contact the Office of Discrimination and Harassment (ODH) at 303-492-2127 or the
Office of Judicial Affairs at 303-492-5550. Information about the ODH, the above referenced
policies and the campus resources available to assist individuals regarding discrimination or
harassment can be obtained at http://www.colorado.edu/odh
(5)
All students of the University of Colorado at Boulder are responsible for knowing and
adhering to the academic integrity policy of this institution. Violations of this policy may include:
cheating, plagiarism, aid of academic dishonesty, fabrication, lying, bribery, and threatening
behavior. All incidents of academic misconduct shall be reported to the Honor Code Council
([email protected]; 303-725-2273). Students who are found to be in violation of the
academic integrity policy will be subject to both academic
sanctions from the faculty member and non-academic sanctions (including but not limited to
university probation, suspension, or expulsion). Other information on the Honor Code can be
found at http://www.colorado.edu/policies/honor.html and at
http://www.colorado.edu/academics/honorcode/
4
Fly UP