Course title

Econometric Analysis II

Responsible person
DAVID Michael WOLF
to enter grades
Numbering Code

Day・Period, etc. Fri1(対面)

開講区分

単位数

1st semester

2.0

Timetable Slot
Code1J370

Lesson topic
Building on foundational topics covered in Econometric Analysis I, this course delves into advanced econometric
topics that go beyond the Ordinary Least Squares (OLS) model. The course will be divided into two parts to achieve
this objective. During the first half, students will focus on how to identify and address problems related toendogeneity using instrumental variables and recent methods developed within the quasi-experimental literature.
During the second half, the class focus will shift to issues often encountered within binary dependent variable
models, including censoring, sample selection and endogeneity within a non-linear setting. Students will also beintroduced to basic machine learning methods that can be used for, amongst other things, model selection.

Lesson target
Students will learn how to identify and address problems related to endogeneity using advanced statistical methods
and some calculus. Students should also expect to develop their analytical and econometric modeling skills, while
also learning how to address issues frequently encountered within limited dependent variable models.

Syllabus and plan
Week 1 – Introduction, Class Expectations and Review
Weeks 2 and 3 – Instrumental Variables
Weeks 4 and 5 – Research Designs with Panel Data (Difference-in-Difference)
Weeks 6 and 7 – Regression Discontinuity Design
Week 8 – Midterm Exam
Week 9 – Maximum Likelihood (Logit and Probit) Review
Weeks 10 and 11 – Truncation, Censoring, and Control Functions
Weeks 12 and 13 – Introduction to Machine Learning
Week 14 – Bootstrapping and Machine Learning Continued
Weeks 15 and 16 – Wrap up, Summary, and Final Exam**Please note, classes scheduled between April 28th and May 14th will be conducted online and on an on-demandbasis. Pre-recorded lecture videos will be provided to students for topics covered during this period.
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
Quizzes (20%)
Midterm Exam (30%)
Final exam (50%)

Evaluation baseline
Students will be assessed on their ability to diagnose and address problems related to endogeneity using either an
instrumental variables approach or another quasi-experimental methodology, such as the difference-in-difference orregression discontinuity method.
Upon completion of the course, students must also be familiar with advanced econometric modeling techniques that go
beyond the basic OLS model. This includes the maximum likelihood estimator and other methods employed to address
sampling bias/censoring within limited dependent variable models.
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)
Exchange students (special auditing students) are allowed to register for this course. All students are expected to
be proficient in calculus (derivatives and integrals) and intermediate-level statistics prior to taking thiscourse.
In addition, students must have completed Econometric Analysis I (or a similar course) if they would like to attend
this class. Students, in other words, should have a solid understanding of the theoretical underpinnings of the OLS
model (i.e., Gauss-Markov assumptions, when these assumptions are violated, etc.) and know how to identify andaddress basic applied econometric problems. This includes, but is not limited to, misspecification errors,
multicollinearity, serial correlation, and heteroskedasticity.
Review and preparation
Students are expected to read the corresponding articles/textbook chapters before coming to class. I will also post
additional review material on the class webpage. Please make sure to check the course website regularly.
I would suggest reviewing mathematics, linear algebra, econometric statistics and your notes from EconometricAnalysis I before the first day of class.

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 discussed in class a second time at home to better your understanding of thecourse material. 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/articles at home.

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

Causal Inference: The Mixtape / Scott Cunningham : Yale University Press ,2021 ,ISBN:9780300251685
An Introduction to Statistical Learning with Applications in R Second Edition / Gareth James, Daniela Witten,
Trevor Hastie, Robert Tibshirani : Springer ,2021 ,ISBN:9781071614174
Classroom Language
EnglishKeywords
Regression Analysis, Difference-in-Difference, Instrumental Variables, Censoring, Sample Selection Bias, Exchange
students (Special auditing students)