COURSE MANUAL
Course name
Empirical Methods in Finance
Course code
323063
Number of credits
6 ECTS
Program
MSc in Finance
Position in Program
Year 2023, Semester 1
Other programs that use this course
//
Academic Year
2023
Language of instruction
English
READ THIS MANUAL THOROUGHLY BEFORE THE START OF THE COURSE
August 28, 2023
☵ Contents
1.Teaching Staff
Name: Vincenzo Pezone
Contact: Room: K608; Email: v.pezone@tilburguniversity.edu
Role in course: Course coordinator, Instructor for Part 1
Profile: https://sites.google.com/site/vpezone/vincenzo-pezone
Name: Frank de Jong
Contact: Room: K614; Email: f.dejong@tilburguniversity.edu
Role in course: Instructor for Part 2
Profile: prof. dr. Frank de Jong | Tilburg University
Name: Joost Driessen
Contact: Room: K607; Email: j.j.a.g.driessen@tilburguniversity.edu
Role in course: Instructor for Part 2
Profile: http://www.joostdriessen.com/
Teaching Assistants:
Ertunç Aydoğdu (e.aydogdu@tilburguniversity.edu)
Sul Kim (S.Kim_2@tilburguniversity.edu)
Marc Stam (m.h.a.stam@tilburguniversity.edu)
August 28, 2023
☵ 2 Course description
2.1 Course description
This course provides an introduction to Financial Econometrics. The course pays large attention to the
intuition and the applicability of a variety of econometric techniques that are widely used in
contemporary empirical research. Reference will be made to many real-world examples from the
corporate finance and asset pricing literature. The classes intend to provide hands-on experience with
the econometric package Stata and will focus on a careful interpretation of the empirical results
obtained.
2.2 Course learning goals
On successful completion of this course, you will be able to:
1. Describe the features of a dataset (e.g., cross section, time series, panel data) and define the relevant
variables.
• This topic requires student to develop the skill to search for and find the appropriate data that
they need to answer a specific research question. It also requires their ability to perform data
manipulation to clean and prepare the dataset for the statistical analysis they will carry on.
2. Interpret descriptive statistics, such as mean, variance, correlation, and recognize the important
variables to use.
• This topic requires students to have the ability to describe and analyze the key relevant
information from the dataset they assembled, and especially to highlight the key features of the
data functional to addressing the research question of interest.
3. Choose the appropriate empirical model (e.g., Event study, OLS, Instrumental Variables, Logit, Probit,
Time series or panel estimators) and perform the tests needed (e.g., t-Test, F-Test, heteroscedasticity,
serial correlation).
• Once students get acquainted with the data, they need to be able to choose the right
econometric methodology to use based on the type of variables in the dataset and on the
research question they are addressing. Moreover, they need to make sure that the methodology
is applied in the correct way, performing all the key statistical tests.
4. Estimate the econometric model using the most convenient estimator
• Students need to estimate the parameters of the model. Further, they should be able to
perform inference under different assumptions about the error term (heteroscedasticity,
autocorrelation, cluster correlation, etc.)
5. Analyze the results and compare different models. Comment and interpret the results obtained, based
on theoretical hypotheses from the literature and on other empirical studies.
• Once the students obtain an output from the econometric method they applied, they are
required to correctly interpret the results of their model and of the tests they performed. The
interpretation should be both descriptive and critical, as from the results students should infer
any potential drawback of their application.
6. Draw conclusions based on the results obtained to answer the research question.
• Students should give an economic interpretation of their results, being able to provide a clear
answer to their research question and to suggest any potential policy implication of their
findings.
7. Perform in- and out-of-sample forecast and select the best forecasting model.
August 28, 2023
• Last, students should be able to deliver forecasts of the explanatory variable and its variance.
Additionally, they need to be able to choose the model that provides the most accurate forecast
according to several criteria (mean squared error, mean absolute error).
2.3 Position in the program
This is a compulsory course for the Master in Finance (1 st year).
2.4 Position in the field
After the bachelor program, you have gained some valuable experiences regarding the studies and life
as a university student. These experiences can be very helpful when facing the challenges of the Master
program. This course has a slightly higher level of rigor, while keeping a focus on topics of practical
relevance.
2.5 Relation with other courses in the program
This course provides the statistical tools to apply the models studied in Advanced Corporate Finance
and Investment Analysis. After the completion of the course, students will be able to interpret empirical
Finance papers that constitute a significant part of the electives in the Master in Finance. Finally, the
programming skills acquired during this course are fundamental to work on the Master Thesis.
2.6 Entry requirements
Finance 1 and 2, as well as some basic mathematical and statistical knowledge are required.
☵ 3 Type of instructions
This course has two types of instructions.
Lectures
The lectures are designed to explain and clarify key concepts and theories. The lectures will build on
scholarly insights and help you familiarize with contemporary financial research, as well as gain a wider
perspective of the field. They provide in-depth explanations, address questions, and discuss examples
of applications.
Tutorials
There are four tutorials in this course that will guide you through Stata examples and the solution to
previous exams.
The instructions will be structured as follows:
• Two lectures per week of 2 hours, each taught by one of the lecturers, except in the weeks
with tutorials.
• Before the midterm and final exams there will be 2 tutorials of 2 hours each, to help students
with the preparation of the exams. The instructions will be taught by one of the teaching
assistants.
• During the first part there will be a STATA mini course of 1 session of 2 hours, taught by one
of the teaching assistants. During this mini-course students will go over the solution of a practice
assignment together with the teaching assistant. This practice assignment (not graded) will be
posted in advance for students to do it on their own.
Study Load
Activity
Estimated number of hours
Lectures and tutorials
44 (58 x 45 minutes)
August 28, 2023
Prepare for lectures and
study material
42 (3 hours per week)
Stata practice assignment
8
Assignments
32
Exam preparation
42
Total
168 hours
☵ 4 Assessment
The final course grade will be composed of various parts:
Graded
assessments
(Paper, oral,
written)
Weight
Which Tool?
(Canvas /
Testvision etc.)
Duration
time
exam
Possibility
for re-sit
Assignment
1
15%
Canvas
//
No
Assignment
2
15%
Canvas
//
No
35%
Testvision
1.5
hours
Yes
35%
Testvision
1.5
hours
Yes
Midterm
Exam
(on campus)
Endterm
exam
(on campus)
• Midterm Exam: At the end of Part 1 of the course there will be a midterm exam covering the
content of Part 1 and counting for 35% of the final course grade. The midterm exam will be a
1.5 hour written examination, on campus, and potentially using Testvision and Stata.
August 28, 2023
• Endterm Exam: At the end of Part 2 of the course there will be a written endterm exam
covering the content of Part 2 and counting for 35% of the final course grade. The exam will be
a 1.5 written examination, on campus, and potentially using Testvision and Stata.
Assignments
There will be two group assignments, each counting for 15% of the final course grade. Groups must be
formed on the Canvas page of the course 2 weeks before the assignment submission deadline at the
latest. Assignment grades from past academic years are not valid. The first assignment will cover the
content of Part 1, the second assignment will cover the content of Part 2. Both assignments are due in
digital form on the Canvas page of the course. Neither late deliveries nor deliveries by email will be
accepted. Every student member of the group must include her/his ANR, name and surname, otherwise
the assignment will be considered as not submitted. You will be given the assignment 2 weeks before
the deadline.
There is no resit opportunity for the assignments.
Final Course Grade
To pass the exam there are 2 conditions:
1: The average of the grades of the midterm and endterm exam needs to be at least 5.00 out of 10.
2: The total course grade needs to be at least 5.50. The total course grade will be determined for 30%
by the grades of the group assignments, for 35% by the grade of the midterm written exam, and for 35%
by the grade of the endterm written exam.
In case you fail to meet requirement 1, your final course grade is equal to the average of the midterm
and endterm exam. In all other situations, your final course grade will be determined for 30% by the
grades of the group assignments, for 35% by the grade of the midterm written exam, and for 35% by
the grade of the endterm written exam.
Computed course grades will be transformed and rounded as necessary to the nearest “whole or half”
point on a scale of 1–10, except that a grade of 5.5 is never given.
4.1 Grading
At least three PhD students will go through each of the assessments (assignments and exams) before
making them available. Moreover, the lecturer who did not design the assessment also reviews the
assessment. Similarly, after grading the assessment, one of the lecturers who did not grade the
assessment goes through the submissions and checks that the grading has been fair and implemented
according to the rubric.
We follow a question-level process for grading. The same grader grades the same question for all
students. Then, she/he changes the order of the exams and grades the following question. If there are
multiple graders each of them is responsible for some questions but two different graders will never
grade the same question.
We communicate the grades of each assessment through Canvas. On the same date, we publish the
instruction for the inspection. Students will have some days to inspect their assessment and feedback
from home and submit their concerns through a Canvas survey. The original grader of the exam will
review each of the concerns. One of the lecturers, who did not grade the assessment, will go through
the submitted concerns in case there are general concerns.
For further details, see the assessment rubric reported in the appendix, which describes the weight given
to each learning goal in the calculation of the final grade.
August 28, 2023
4.2 Resit
The resit consists of a 3 hour on campus exam, covering the material of both the first and second half of
the course. If you have failed the course in the first round, you have to do the full resit exam. It is not
possible to take the midterm exam grade or the endterm exam grade to the resit round.
To pass the exam in the resit round there are 2 conditions:
1: The grade for the resit exam needs to be at least 5.00 out of 10.
2: The total course grade needs to be at least 5.50. The total course grade will be determined for 30%
by the grades of the group assignments and for 70% by the grade of the resit exam.
In case you fail to meet requirement 1, your final course grade is equal to the grade of the resit exam.
In all other situations, your final course grade will be determined for 30% by the grades of the group
assignments, and for 70% by the grade of the resit exam.
Computed course grades will be transformed and rounded as necessary to the nearest “whole or half”
point on a scale of 1–10, except that a grade of 5.5 is never given.
4.3 Repeaters rule
Repeaters are assessed in the same way as new students. Past assessments, including exams and
assignments, do not count for the grade.
4.4 Working together
The assignments will be done in groups consisting of at least three and at most four students. To avoid
that any student be left without a group, students who have not joined a group or that have joined a
group that does not meet these criteria will be randomly allocated to meet these criteria by the
instructors by the group formation deadline. The groups for the first and second assignment might be
different.
☵ 5 Code of Conduct
5.1 Code of Conduct
Tilburg University has a Code of Conduct that all employees and students at Tilburg University are
expected to follow. Please read this code of conduct.
5.2 Academic Integrity
Cheating, plagiarism, and/or doing work for another person who will receive academic credit are all
impermissible. This includes the use of unauthorized books, notebooks, or other sources in order to
secure or give help for an assignment or the presentation of unacknowledged material as if it were the
student’s own work. Having unauthorized notes at your exam, cribbing from a fellow student,
manipulating results, and copying text from others without references are examples of fraud. Once fraud
is suspected, the Examination Board will be informed accordingly. Please refer to the website for more
information. For group assignments, it is of course allowed to cooperate within the group, but not across
groups.
☵ 6 Course structure
Week
Date
Lecture/Tutorial on …
How to prepare?
August, 29
Introduction:
- Course organization (material,
tutorials, software, assignments,
exam)
- Introduction to the topics
(W) ch. 1
1
August 28, 2023
1
August, 30
Maths and Stats:
- Maths and stats foundations
- Descriptive statistics
(W) appendix A-D
2
September, 5
Bivariate CLRM:
- Bivariate regression model
- OLS assumptions and properties
Stata lectures 1, 2; (W) ch. 2
2
September, 6
Bivariate CLRM:
- Standard errors
- Goodness of fit
(W) ch. 3, 5
3
September, 12
Bivariate CLRM:
- Hypothesis testing: t-test
Stata lecture 3; (W) ch. 4
3
September, 13
Multivariate CLRM:
- Multivariate regression model
- Multivariate OLS assumptions
and properties
(W) ch. 3, 4
4
September, 19
Multivariate CLRM:
- Hypothesis testing: F-test
(W) ch.6, 7
4
September, 20
Multivariate CLRM:
- Dummy variables
(W) ch. 7
5
September, 25
Stata Minicourse (taught by the
Teaching Assistant):
- Basic commands and examples
with Stata
5
September, 26
Causal Inference:
- Instrumental variables and
difference-in-difference
(W) ch. 7, 8, 15; (RW) ch.1, 2,
3, 4; (BNPW)
6
October, 3
Causal Inference:
- Regression discontinuity design
(W) ch. 7; (RW) ch. 4, 5; (A);
(CR)
6
October, 4
Exam Preparation Instruction
(taught by the Teaching Assistant):
- Solution of past exams with
teaching assistant
7
October, 10
Discrete Choice Models:
- Logit, Probit, Tobit models
(W) ch.7, 17
7
October, 11
Nonparametric Approaches:
- Bunching
(K)
8
October, 25
Event studies
Reader
8
October, 26
Event studies
Reader
9
November, 1
Event studies
Reader
9
November, 2
Event studies / Machine learning?
Reader
August 28, 2023
10
November, 7
Time-series: Introduction and
revisit CLRM
(B) ch. 2.4.1 and (B) ch. 5
10
November, 9
Time-series: Introduction and
revisit CLRM
(B) ch. 5
11
November, 14
Time-series models and forecasting
(B) ch. 6
11
November, 16
Time-series models and forecasting
(B) ch. 7.10, 7.11, 7.14
12
November, 21
Trends, random walk, unit root &
time-varying volatility
(B) ch. 8.1
(B) ch. 9.2, .9.3, 9.4, 9.5, 9.6,
9.7
12
November, 23
Tutorial
13
November, 28
Panel Data: Introduction
(B) ch. 11.1, 11.2, 11.3
13
November, 30
Panel Data: Estimation
(B) ch. 11.4, 11.6
14
December, 5
Panel Data: Fama-MacBeth &
Exam practice
(B) ch. 14.2
14
December, 7
Tutorial
Stata web-lectures 1 and 2 should be watched after week 1. Stata web-lecture 3 should be watched after
week 2.
In line with TISEM policy, lectures will be recorded and made available one week before the exam.
6.1 Summary of deadlines
Date
Deadline for
Format
13 October, 2023
First assignment
Online submission
TBA
Second assignment
Online submission
TBA
Midterm exam
In class exam
TBA
Endterm exam
In class exam
TBA
Resit exam
In class exam
☵ 7 List of materials
- Lecture notes, lecture video clips, and papers will be made available via Canvas.
- Main textbooks:
➢
(W) Wooldridge J.M., “Introductory Econometrics: A Modern Approach”, (better latest edition);
Supplemental material (datasets, exercises) will be posted on Canvas
August 28, 2023
➢
(B) Brooks C., "Introductory Econometrics for Finance", 4th edition (earlier editions also
possible)
o
o
➢
Some sections of the chapters covered will be indicated as “read only”, these sections
are not required for exam preparation. These read-only sections will be announced on
Canvas.
This webpage contains additional questions, datasets, quizzes, and solutions for the
textbook of Brooks: Brooks student resources
For the event studies part, a reader will be made available via Canvas.
- Additional textbooks for background reading:
* (RW) Roberts, M.R., and Whited T. (2013), “Endogeneity in Empirical Corporate Finance”,
Handbook of the Economics of Finance.
* (BNPW) Bennedsen, M., Nielsen, K., Perez-Gonzalez, F., and Wolfenzon, D. (2007), “Inside
the Family Firm: The Role of Families in Succession Decision and Performance”, Quarterly
Journal of Economics, 122, pp. 647-691.
* (A) Agrawal, A. (2013), “The impact of investor protection law on corporate policy and
performance: Evidence from the blue sky laws”, Journal of Financial Economics, 107 (2), pp.
417- 435.
* (CR) Chava S., and Roberts M.R. (2008), “How Does Financing Impact Investment? The Role
of Debt Covenants”, Journal of Finance, LXIII (5), pp. 2085-2121.
* (K) Kleven H. J. (2016), “Bunching”, Annual Review of Economics, 8, pp. 435-464.
- Additional textbook: this is not exam material, but it may be useful for the Msc thesis or other research:
* (V) Verbeek, M. (2021) - Panel Methods for Finance: A Guide to Panel Data Econometrics
for Financial Applications, de Gruyter Studies in the Practice of Econometrics, available
online for free via the Tilburg University library,
https://www.degruyter.com/document/doi/10.1515/9783110660739/html.
- Three Stata web lectures will be posted on Canvas for self-study. The course schedule below suggests
the optimal time to watch them. Students are expected to practice what they learn in the web lectures.
These web lectures are:
1. Getting started with Stata
2. Managing and editing datasets
3. OLS regression analysis
- Some extra background material to learn how to use Stata is available at:
• Baum C., "An Introduction to Modern Econometrics Using Stata", Stata Press
• http://dss.princeton.edu/online_help/stats_packages/stata/stata.htm
- Information on how to install Stata will be posted on Canvas
- Students have access to a class user account available for the WRDS data library. The system can be
accessed at: http://wrds-web.wharton.upenn.edu/wrds/index.cfm?true, with the credentials:
Username: emf2023_2024, Password: EMF_WRds2023!
August 28, 2023
August 28, 2023