＜概要/Course Content Summary＞
This course provides a general introduction to econometrics, a field that uses mathematical economics, economic data, and statistical inference to test economic theories empirically. In particular, as economic data mostly cannot be generated by a controlled experiment, a special set of statistical tools has to be applied in the quantitative analysis of economic phenomena. This course considers the practical use of these tools using a hands-on approach.
＜到達目標/Goals,Aims＞
The key objective of this course is to familiarize students with how regression analysis can be used in quantitative research, whether it relates to economics or another social science. This practical knowledge should be useful to students as they begin developing and completing research projects in their advanced seminars. In addition, as work in economics, finance, management, marketing, political science, etc., is becoming increasingly quantitative in nature, a general understanding of econometric techniques should also be an asset to students after graduation.
This course will begin with a basic introduction to statistics, but students who have taken the Introduction to Quantitative Research Methods course will have an advantage. Also, a basic knowledge of how to use a computer is assumed. Thus, students who have taken the Library and Information Science Skills and Practicum courses will have an easier time.
＜授業計画/Schedule＞
（実施回/
Week）
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（内容/
Contents）
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（授業時間外の学習/
Assignments）
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（実施回/ Week）
Week 1
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（内容/ Contents）
Introduction
Topics: Course description, basic statistics, mean, variance and correlation
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（授業時間外の学習/ Assignments）
Read Chapter 1 and Appendices A, B, C, D, and E (45 minutes)
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（実施回/ Week）
Week 2
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（内容/ Contents）
Learning Useful Stata Commands
Topics: Gaining familiarity with Stata
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（授業時間外の学習/ Assignments）
Read Chapter 2 and review Week 1 Notes (45 minutes)
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（実施回/ Week）
Week 3
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（内容/ Contents）
Important Probability Distributions
Topics: Normal distribution, t-distribution, chi-squared distribution, F distribution
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（授業時間外の学習/ Assignments）
Read Appendices F, G, and H and review Week 2 Notes (45 minutes)
Assignment 1 due.
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（実施回/ Week）
Week 4
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（内容/ Contents）
Statistical Inference
Topics: Hypothesis testing, confidence intervals
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（授業時間外の学習/ Assignments）
Review Week 3 Notes (45 minutes)
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（実施回/ Week）
Week 5
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（内容/ Contents）
In-class Project
Topics: Work on in-class project
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（授業時間外の学習/ Assignments）
Review Week 4 Notes (45 minutes)
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（実施回/ Week）
Week 6
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（内容/ Contents）
Bivariate Ordinary Least Squares
Topics: bivariate ordinary least squares, random variation in coefficient estimates, exogeneity and unbiasedness, goodness of fit
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（授業時間外の学習/ Assignments）
Read Chapter 3 (minimum 90 minutes)
Assignment 2 due.
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（実施回/ Week）
Week 7
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（内容/ Contents）
Hypothesis Testing and Interval Estimation
Topics: hypothesis testing, t tests, p values, confidence intervals
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（授業時間外の学習/ Assignments）
Read Chapter 4 (minimum 90 minutes)
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（実施回/ Week）
Week 8
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（内容/ Contents）
In-class Project
Topics: Work on in-class project
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（授業時間外の学習/ Assignments）
Review Week 7 Notes (45 minutes)
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（実施回/ Week）
Week 9
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（内容/ Contents）
Multivariate Ordinary Least Squares
Topics: multivariate ordinary least squares, omitted variable bias, measurement error, goodness of fit, model specification
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（授業時間外の学習/ Assignments）
Read Chapter 5 (minimum 90 minutes)
Assignment 3 due.
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（実施回/ Week）
Week 10
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（内容/ Contents）
Multivariate Ordinary Least Squares
Topics: goodness of fit, model specification
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（授業時間外の学習/ Assignments）
Review Week 9 Notes (45 minutes)
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（実施回/ Week）
Week 11
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（内容/ Contents）
Dummy Variables
Topics: introduction to dummy variables, using bivariate OLS to assess difference of means, dummy variables in multivariate OLS, interaction variables
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（授業時間外の学習/ Assignments）
Read Chapter 6 (minimum 90 minutes)
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（実施回/ Week）
Week 12
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（内容/ Contents）
In-class Project
Topics: Work on in-class project
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（授業時間外の学習/ Assignments）
Review Week 10 and Week 11 Notes (60 minutes)
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（実施回/ Week）
Week 13
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（内容/ Contents）
Transforming Variables
Topics: quadratic and polynomial models, reciprocal models, log-linear models, hypothesis testing about multiple coefficients
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（授業時間外の学習/ Assignments）
Read Chapter 7 (minimum 90 minutes)
Assignment 4 due
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（実施回/ Week）
Week 14
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（内容/ Contents）
Making predictions
Topics: generating confidence intervals from regression results
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（授業時間外の学習/ Assignments）
Review Week 13 Notes (45 minutes)
Assignment 4 due.
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（実施回/ Week）
Week 15
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（内容/ Contents）
In-class Project
Topics: Work on in-class project
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（授業時間外の学習/ Assignments）
Review notes from Week 14 (45 minutes)
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Course Requirements and Assignments
You are required to attend at least 80% of the classes and complete a number of in-class exercises and homework assignments. A final report is due at the end of semester.
＜成績評価基準/Evaluation Criteria＞
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Attendance
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10%
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Homework Assignments
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50%
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Final Report
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40%
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Classroom Policies
a) While students are encouraged to study together, every student must individually complete and submit each of the homework assignments. In cases where two or more students submit an identical assignment, all of the students concerned will be given a grade of zero on that assignment.
＜テキスト/Textbook＞
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Michael A. Bailey
, Real Stats
:
Using Econometrics for Political Science and Public Policy
,
1st edition
.
(Oxford University Press, 2016)
.
ISBN:978-0-19-998194-6
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＜備考/Remarks＞
INSTRUCTOR: Colin Davis
OFFICE: SK323
TELEPHONE: 251-4971
EMAIL: cdavis@mail.doshisha.ac.jp
OFFICE HOURS: to be announced
Note: This syllabus will be subject to changes and/or revisions.
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