Download EIB E213: Econometrics The Fletcher School, Tufts University Fall 2013

EIB E213: Econometrics
The Fletcher School, Tufts University
Fall 2013
Location: C205, M-W, 13:30-15:10
Draft: Final Syllabus will be Distributed on the First Day of Class
Instructor’s Information:
Jenny C. Aker, Assistant Professor of Development Economics, The Fletcher School
Email:
[email protected] or [email protected]
Web Page:
http://sites.tufts.edu/jennyaker/
Office Hours:
M-W 3:30-5 or by appointment
Office Address:
Cabot 603C
Teaching Assistant: Jing Cai
Staff Assistant:
Sheri Callender
For any questions about concepts, assignments or data, please sign up for office hours or see me right
after class. If you cannot make office hours during the pre-assigned time slot, please e-mail me with
the header “Office Hours Meeting”. I will not be able to respond to individual e-mails with questions
about readings, class concepts, assignments or tests.
Course Description
This course provides an introduction to basic econometric methods. These are the tools of data
analysis that economists and other social scientists use to estimate the size of economic and social
relationships and to test hypotheses about them, using real-world data. The goal of this course is to
equip students with the facts, intuition and skills necessary to critically read econometric research
produced by others and to conduct independent econometric research. Coursework includes quizzes,
problem sets, midterm and final examinations and a group econometrics research project. The problem
sets will require students to use the statistical software package, STATA, which is available in the
Mugar computer lab. Reasonably priced versions of the software are also available for students (and is
recommended so that you do not need to plan your study groups around the Mugar computer lab
hours).
Pre-requisites: Introductory statistics (at the level of EIB B205) is required. Basic multivariable
calculus (at the level of EIB E210m) and introductory economics are strongly recommended but not
required. All relevant statistical concepts will be reviewed as they arise, but the reviews will be brief.
The basic calculus concepts employed in the course pertain to derivatives and what they tell us about
the shape of relationships between variables in simple graphs.
To assess whether your background is adequate, review the appendices on probability and statistics in
the Wooldridge textbook, with a particular focus on probability density functions (pdfs), a concept that
will arise repeatedly in class. In addition, you should plan on purchasing Naked Statistics: Stripping
the Dread from the Data (Charles Wheelan), which provides an intuitive and useful overview of
statistical and econometric concepts. I will offer a (brief) statistics review session online, as well as
two additional review sessions throughout the semester.
Requirements and Grading: There will be five online quizzes, five problem sets, a midterm, an inclass final exam and an econometrics research policy brief. Class sessions will be primarily lecture1
based and will rely heavily on class notes. Students are also expected to prepare for class by
completing the required readings before each class and actively participating in class discussion.
Lecture slide handouts will be posted on Trunk the day of class. Grades will be based upon the
following breakdown:
Problem sets:
Quizzes:
Midterm exam:
Final exam:
Policy brief:
15%
5%
35%
40%
10%
The problem sets can be completed in groups of no more than five (5) people and no fewer than three
people. The midterm and final examinations will be closed book exams, but students will be allowed
to prepare and use one 3X5 index card of formulas and notes for each exam. Timed quizzes will be
completed online and will be individual. The policy brief will be in groups of two students, and will
require a written document and an in-class presentation.
Textbooks
The required textbook for this course is: Wooldridge, Jeffrey, Introductory Econometrics: A Modern
Approach, Fourth edition, South-Western College Publishing. The fifth edition was recently released
in 2013, but is not required for this course. W4 refers to the fourth edition of Wooldridge in the
readings.
Reading an econometrics text is never easy, but it is essential that you make the effort to read this
book. In the past, students have primarily relied upon the notes and not used the book (and many
students will probably tell you not to purchase the book). This is your opportunity to learn the “written
language” of econometrics. As with learning any language, reading goes very slowly at first, as you
learn what the various symbols mean. If you make consistent effort, by the end of the semester you
will find that your reading speed and comprehension have improved greatly. Developing such skills is
of great value, because this course is only an introduction to econometrics.
An additional required textbook is Charles Wheelan, Naked Statistics: Stripping the Dread from the
Data, 2013, WW Norton and Company.
Lecture Notes and Trunk. The lecture slides, problem sets, data, study questions and quizzes will be
posted on the Trunk web pages for the course. Lecture slides will be posted the day of class. You
should be enrolled automatically in the Trunk site shortly after you register for the course. A separate
calendar with the specific date for each topic will also provided. Answer keys will not be provided for
the study questions.
Important or Unusual Dates
Due to travel for fieldwork in Niger, there might be one class during the semester that will be cancelled
(and rescheduled) in October or November, usually around the time of another school holiday (such as
Columbus Day or Veterans’ Day). Students will be informed of this cancellation and re-scheduling
approximately two weeks in advance.
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Course Outline
I.
Introduction: What is econometrics? What is it good for?
Wheelan, Introduction, Chapters 1, 2, Conclusion
II.
The simple two-variable linear regression model
A. Statistics review 1: Probability, random variables, expected values, variances
W4, Appendix B, p. 714-737
Wheelan, Chapters 4, 5, 5½, 7
B. Ordinary Least Squares (OLS) as a method for fitting the model to data
W4, Chapter 2, p. 22-39; Appendix A, p.695-702
Wheelan, Chapter 11 (omitting the Appendix)
C. The R-squared goodness of fit measure
W4, Chapter 2, p.40-41
D. Statistics Review 2: Estimators and Desirable statistical properties for estimators
W4, Appendix C, p. 747-759
E. OLS estimators as random variables
F. Classical assumptions under which OLS estimators have the desirable properties
W4, Chapter 2, p.46-59
III.
Multiple (k) variable regression models
A. Introduction to multiple regression analysis
W4, Chapter 3, p.68-73
B. OLS and goodness of fit in the K-Variable Model
W4, Chapter 3, p.73-83, Chapter 6, p. 199-205
C. The classical assumptions revisited
W4, Chapter 3, p. 84-104, W3, Chapter 3, p.89-109
D. Functional transformations of dependent and independent variables
W4, Appendix A, p.702-711 ; Chapter 2, p. 43-46 ; Chapter 6, p. 189-199
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E. Dichotomous (binary) independent variables
W4, Chapter 7, p. 225-246; W3, Chapter 7, p.231-252 (W2, Chapter 7, p.218-240)
F. Units of Measurement
W4, Chapter 2, p. 41-43; Chapter 6, p. 184-189
G. Model specification
IV.
Interval estimation and hypothesis testing
A. Statistics review 3: Common families of statistical distributions
W4, Appendix B, p. 737-744
Wheelan, Chapter 11 (Appendix)
B. OLS under the normality assumption
W4, Chapter 4, 117-120
Wheelan, Chapter 8
C. Confidence intervals and interval estimation
W4, Chapter 4, p. 138-140; Appendix C, p. 762-769
Wheelan, Chapter 10
D. Testing hypotheses about a single parameter: the t test and statistical significance
W4, Chapter 4, p. 120-138; Appendix C, p. 770-782
Wheelan, Chapter 9
E. The distinction between statistical significance and economic importance
W4, Chapter 4, p. 135-138; Appendix C, p. 780-781
F. Testing hypotheses involving several parameters: the F test
W4, Chapter 4, p. 140-154
G. Presentation of regression results
W4, Chapter 4, p. 154-156
V. Models for binary dependent variables
A. OLS when the dependent variable is dichotomous
W4, Chapter 7, p. 246-251
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B. Statistics review: maximum likelihood as an approach to creating estimators
W4, Appendix C, p. 760-762
C. Probit and logit regression models for dummy dependent variables
W4, Chapter 17, p. 574-580
D. Interpreting coefficients in probit and logit models
W4, Chapter 17, p. 580-587
E. Interval estimation and hypothesis testing in probit and logit models
VI. Omitted Variable Bias
A. Nature of the problem and description of its consequences
W4, Chapter 3, p. 89-94
Wheelan, Chapter 12
B. Omitted variable bias in program evaluation
W4, Chapter 7, p. 251-254; W3, Chapter 7, p.258-260 (W2, Chapter 7, p.246-248?)
C. Dealing with OVB: Considering whether the bias “works in your favor”
D. Dealing with Omitted Variable Bias: Introducing proxy measures
W4, Chapter 9, p. 306-313
E. Dealing with OVB: Using true or natural experiments
W4, Chapter 13, p. 444-455
F. Dealing with OVB: Using fixed effects methods in panel and pseudo-panel data to
eliminate bias
W4, Chapter 13, p. 455-470 ; Chapter 14, p. 481-489
G. Dealing with OVB: Using instrumental variables techniques
W4, Chapter 15, p. 506-525
VII. Other Problems with the Dependent and Independent Variables
A. Including irrelevant variables
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W4, Chapter 3, p. 89
B. Measurement error in the dependent and independent variable
W4, Chapter 9, p. 315-322
C. Multicollinearity and other data weaknesses
W4, Chapter 3, p. 94-99
VIII. The Problem of Heteroskedasticity
W4, Chapter 8
IX. Summing Up
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