Course title

Econometric Analysis I

Responsible person
DAVID Michael WOLF
to enter grades
Numbering Code

Day・Period, etc. Thurs2(対面)

開講区分

単位数

2nd semester

2.0

Timetable Slot
Code3J370

Lesson topic
This course focuses on the application of statistical methods to the testing and estimation of economic
relationships. Students will begin by learning the underlying assumptions of the ordinary least squares (OLS) modeland the Gauss-Markov Theorem. Students will then learn how to apply OLS to conduct hypothesis tests. Common
violations of the Gauss-Markov assumptions and limitations of the OLS model are then explored, including
multicollinearity, heteroscedasticity, serial correlation, omitted variable bias, and binary dependent variables.
Solutions and limitations associated with each issue are then discussed.

Lesson target
Upon completion of this course, students should be able to understand how to formulate and conduct hypothesis tests
using OLS. Students should be able to understand when OLS is likely to fail (i.e., produce biased and/orinefficient estimates), diagnose and remedy basic econometric problems, and code basic commands in R Studio.

Syllabus and plan
Topics:Weeks 1 - 2: Gauss-Markov Theorem and R Studio
Weeks 3 - 4: Sampling Distribution of the OLS Estimator, Interpreting the OLS Model, and Properties of Inefficientand Biased Estimators
Weeks 5 - 6: Standard Errors, Singular vs Joint Hypothesis Tests, and Type I/Type II Error
Weeks 7 - 8: Functional Form Decisions, Measures of Model Fit, and Concerns with Out-Of-Sample Prediction
Week 9: Midterm Exam
Weeks 10 - 11: Multicollinearity, Serial Correlation, and Heteroskedasticity
Weeks 12 - 13: Panel Data, Fixed Effects Models, and Difference-in-Difference Models
Weeks 14: Binary Dependent Variable Models and Discrete-Choice Modeling
Weeks 15 - 16: Wrap-up, Summary, and Final Exam
The schedule is subject to change as the semester progresses. This class will be conducted in principle face-toface. I will notify you via Google Classroom and mention in class beforehand if a class will be conducted online as
opposed to in person.
In addition, due to university regulations the final exam will be conducted in person and will be scheduled during
the 15th or 16th week of the course. During the non-exam week (i.e., either the 15th or 16th week), a final classwill be held online or on-demand.
Evaluation method
Homework and quizzes (20%)
Midterm exam (30%)
Final exam (50%)

Evaluation baseline
Students will be assessed on their ability to understand the basic assumptions underlying the Gauss-Markov Theorem,
when these assumptions are likely to fail, and how to apply basic corrections to the OLS model to remedy some ofthese problems. Proficiency in R Studio (i.e. cleaning, merging and correcting errant data) will also be assessedin homework assignments.
Homework assignments will contain empirical exercises that must be completed in R Studio. You must upload youroutput and R code onto the Google Classroom webpage to receive credit.
The final and midterm exams will be closed book. No notes are allowed during either exam. The final exam will cover
all topics covered in Week 1 and onwards.
The grading scale used for this course is:
90 - 100: S
80 - 89: A
70 - 79: B
60 - 69: C<60: FailNotice (include info. on related class)Students should have a taken several classes in calculus and statistics prior to taking this course. Students thatlack a strong mathematical background in these topics will find this course cumbersome.
Exchange students (special auditing students) are allowed to register for this course. All students are expected tohave knowledge of differential calculus and intermediate-level statistics prior to taking this course.
Students must also have access to their own personal laptop computer and be able to bring this computer to theclassroom. Computers are required for this course as students will be learning how to code in the program R.

Review and preparation
Students are expected to read the corresponding chapters from the course textbook before coming to class. I will
also post additional review material on the course website. Please make sure to check the website regularly.
I would suggest reviewing mathematics, linear algebra and econometric statistics before the first day of class.
Reading up on how to use R Studio is also highly recommended.

Office hour・Contact information
Office hours: by appointment only
Office location: Frontier Hall for Social Sciences Room 812
Email: wolf@econ.kobe-u.ac.jp

Message for student
Please work through the examples in class using R Studio and feel free to ask questions if you are ever confused.
You will find this course cumbersome if you do not read the corresponding textbooks chapters and go through the R
exercises at home.

Improvemenets in TeachingText

AE Introductory Econometrics: A Modern Approach 7th Edition / Jeffery Wooldridge : Cengage Learning ,2020,ISBN:9789814866088
Reference Material

Mathematics for Economists / Carl P. Simon, and Lawrence Blume : W. W. Norton & Company ,1992 ,ISBN:9780393957334
Classroom Language
EnglishEnglishKeywords
Regression Analysis, Gauss-Markov Theorem, Panel Data, R Studio, Exchange Students (Special auditing students)