Spring 05
Instructor:
Misa Nishikawa
Location:
Lecture WB139, Lab WB213
Time:
Th
Office: NQ228
E-mail:
mnishikawa@bsu.edu
Office
hours: Tue 11a-12p, Wed 4p-5p, Thu 4p-5:30p, and by appointment
The
course introduces students basic econometrics,
including statistical inference, regression analysis and maximum likelihood
estimation. The course is also designed to teach students how to apply these
techniques to real-world research questions in political science and public
policy analysis.
Required Texts and
Jeffrey
M. Wooldridge. 2003. Introductory Econometrics: A Modern Approach, 2e.
SPSS
Student version (recommended)
Simple
Guide to SPSS (recommended)
Supplementary reading in the form of additional
handouts and Internet resources
Grades
Grade is allocated in the following way:
|
Exam 1, 2, and 3 |
(25%,25%,10%)
60% |
|
Assignments |
6% |
|
Participation/Discussion |
9% |
|
Paper |
25% |
|
Totals |
100% |
Grading
Scale:
|
A |
93.0-100% |
|
A- |
90.0-92.9% |
|
B+ |
85.0-89.9% |
|
B |
76.0-84.9% |
|
B- |
70.0-75.9% |
|
C+ |
65.0-69.9% |
|
C |
60.0-64.9% |
|
C- |
58.0-59.9% |
|
D+ |
55.0-57.9% |
|
D |
52.0-54.9% |
|
D- |
49.0-51.9% |
|
F |
-48.9 |
Make-up exams will not be
given
except in the case of extreme circumstances.
The student must be able to provide documentation that the absence is
unavoidable (e.g., illness, death in the family, observance of a religious
holiday) and make arrangements with me prior
to the scheduled exam dates.
Exams
will be based on material covered during class in lectures or class discussions
and from the required readings.
A
research paper of 25 double spaced typed pages (with 12 point font size and one
inch margins) is due on April 21. You are required to use the statistical
techniques that you learn in this course. You will also be given additional
assignments. No extensions will be granted.
Ten points will be
subtracted from your grade for each day that the paper/assignments are late.
They will not be accepted beyond three days from the due date.
Participation in class discussions is
strongly encouraged. Of course,
participation should be constructive, and all comments should be relevant to
the material being covered in class.
Students must do all of the
reading! Respect should be shown for
all other class members at all times. Inappropriate and disruptive participation/behavior
will result in a drop in the student’s grade.
For the lab hours, students are expected to use only appropriate software, which typically does not include the internet or e-mail programs. Engaging in these behaviors will result in a drop in the student’s grade.
Students
will be responsible for knowing any changes made to the syllabus during class
time whether they were in attendance or not.
The instructor’s lecture notes are not available to students; it is the
student’s responsibility to obtain class notes from a classmate, should class
be missed. Late work will be downgraded by 10 points each day it
is late.
If
you need course adaptations or accommodations because of a disability, if you have
emergency medical information to share with me, or if you need special
arrangements in case the building must be evacuated, please make an appointment
with me as soon as possible.
Academic Honesty:
Honesty,
trust, and personal responsibility are fundamental attributes of the university
community. Academic dishonesty by a student will not be tolerated, for it
threatens the foundation of an institution dedicated to the pursuit of
knowledge. To maintain its credibility and reputation, and to equitably
assign evaluations of scholastic and creative performance, Ball State
University is committed to maintaining a climate that upholds and values the
highest standards of academic integrity. It goes without saying that
cheating and plagiarism will result in failing the course.
|
Week |
Date |
Topic |
|
|
Week
1 |
Jan
13 (Thu) |
Review |
|
|
Week
2 |
Jan
20 (Thu) |
Data
|
Chapter
1 |
|
Week
3 |
Jan
27 (Thu) |
Simple
regression I |
Chapter
2 |
|
Week
4 |
Feb
3 (Thu) |
Simple
regression II |
|
|
Week
5 |
Feb
10 (Thu) |
Multiple
regression analysis: Estimation |
Chapter
3 |
|
Week
6 |
Feb
17 (Thu) |
Multiple
regression analysis: Inference |
Chapter
4 |
|
Week
7 |
Feb
24 (Thu) |
Exam 1
|
|
|
Week
8 |
Mar
3 (Thu) |
Multiple
regression analysis with Qualitative Information: Binary variables |
Chapter
7 |
|
Week
9 |
Mar
10 (Thu) |
Break |
|
|
Week
10 |
Mar
17 (Thu) |
Diagnostics |
Chapters
8 and 9 |
|
Week
11 |
Mar
24 (Thu) |
Basic
Regression Analysis with Time Series Data |
Chapter
10 |
|
Week
12 |
Mar
31 (Thu) |
Limited
Dependent Variable Models |
Chapter
17 |
|
Week
13 |
Apr
7 (Thu) |
To
be announced |
|
|
Week
14 |
Apr
14 (Thu) |
Exam 2
|
|
|
Week
15 |
Apr
21 (Thu) |
Presentation
I Paper due |
|
|
Week
16 |
Apr 28 (Thu) |
Presentation
II |
|
|
|
Final |
Exam
3 |
|