EPOKA UNIVERSITY
FACULTY OF ECONOMICS AND ADMINISTRATIVE SCIENCES
DEPARTMENT OF BANKING AND FINANCE
COURSE SYLLABUS
2023-2024 ACADEMIC YEAR
COURSE INFORMATIONCourse Title: FINANCIAL ECONOMETRICS II |
| Code | Course Type | Regular Semester | Theory | Practice | Lab | Credits | ECTS |
|---|---|---|---|---|---|---|---|
| BAF 334 | C | 6 | 3 | 0 | 0 | 3 | 6 |
| Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature) | Prof.Dr. Güngör Turan gturan@epoka.edu.al |
| Main Course Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours: | M.Sc. Egla Mansi emansi@epoka.edu.al , TBA |
| Second Course Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours: | NA |
| Language: | English |
| Compulsory/Elective: | Elective |
| Study program: (the study for which this course is offered) | Bachelor in Banking and Finance (3 years) |
| Classroom and Meeting Time: | Check timetable |
| Teaching Assistant(s) and Office Hours: | NA |
| Code of Ethics: |
Code of Ethics of EPOKA University Regulation of EPOKA University "On Student Discipline" |
| Attendance Requirement: | 75% |
| Course Description: | - |
| Course Objectives: | This econometrics course is designed to provide students with the essential skills and knowledge necessary for conducting rigorous research in economics and finance. By delving into advanced econometric practices and models, students will gain the expertise needed to analyze complex economic phenomena using empirical data. Through the utilization of the STATA and R econometric packages, students will engage in hands-on learning experiences, mastering techniques for data manipulation, estimation, and interpretation of results. Moreover, laboratory sessions will offer students practical opportunities to apply theoretical concepts to real-world datasets, fostering a deeper understanding of econometric methodologies. By the course's conclusion, students will emerge equipped with the ability to conduct independent research, critically evaluate empirical findings, and contribute meaningfully to the advancement of economic and financial knowledge. |
|
BASIC CONCEPTS OF THE COURSE
|
| 1 | Dependent Variables and Binary Outcomes |
| 2 | Measurement Error and Proxy Variables |
| 3 | Panel Data Analysis |
| 4 | Instrumental Variable |
| 5 | Difference-in-Differences (DID) Methods |
| 6 | Resampling Methods |
| 7 | Simultaneous Equations |
| 8 | Multinomial Variable Models |
| 9 | Regression Discontinuity Design (RDD) |
|
COURSE OUTLINE
|
| Week | Topics |
| 1 | Introduction to the: Course, Syllabus, Textbook and Evaluation Method. |
| 2 | Binary Dependent Variable, chapter 14 at Cameron and Trivedi, pg 463: This chapter considers the simplest case of binary outcomes, where there are two possible outcomes. Examples include whether or not an individual is employed and whether or not a consumer makes a purchase |
| 3 | Proxy variables and measurement error, chapter 9 Wooldrige, pg.294: In this chapter, we return to the much more serious problem of correlation between the error, u, and one or more of the explanatory variables. |
| 4 | Instrumental Variable, chapter 15 Wooldrige and chapter 4 Angrist and Pischke, pg.495: IV methods were pioneered to solve the problem of bias from measurement error in regression models. One of the most important results in the statistical theory of linear models is that a regression coefficient is biased towards zero when the regressor of interest is measured with random errors. Instrumental variables methods can be used to eliminate this sort of bias. |
| 5 | Panel Data and chapter 21 cameron and trivedi, pg. 697: panel data, also called longitudinal data, contain periodically repeated observations of the same subjects, they have a large potential for resolving issues that cross-section models cannot satisfactorily handle. |
| 6 | Difference-in-Difference Methods, chapter 9 in Cunningham: The difference-in-differences design is an early quasi-experimental identification strategy for estimating causal effects that predates the randomized experiment by roughly eighty-five years. In this chapter, I will explain this popular and important research design both in its simplest form, where a group of units is treated at the same time, and the more common form, where groups of units are treated at different points in time. |
| 7 | Simultaneous equations, chapter 16 Wooldridge, chapter 2 C&T, pg.534: The objective is to bring into the discussion several key ideas and concepts that have more general relevance. Although the analysis is restricted to linear models, many insights are routinely applied to nonlinear models. |
| 8 | Limited dependent variable models, chapter 17 Wooldrige, pg.559: we studied the linear probability model, which is simply an appli<.>ation of, the multiple regression model to a binary dependent variable. A binary dependent vamable is an example of a limited dependent variable (LDV). Logit/Probit |
| 9 | Midterm |
| 10 | Multinomial variable models, chapter 17 Wooldrige and chapter 15 C&T, pg.559: The preceding chapter considered models for discrete outcome variables that can take one of two possible values. Here we consider several possible outcomes, usually mutually exclusive. |
| 11 | Regression Discontinuity Design, chapter 6 Cunningham: The reason RDD is so appealing to many is because of its ability to convincingly eliminate selection bias. This appeal is partly due to the fact that its underlying identifying assumptions are viewed by many as easier to accept and evaluate. |
| 12 | Resampling Methods, chapter 10 in Hansen: Dealing with Bootstrap Algorithm/Regression and Cross Validation |
| 13 | Project Presentations |
| 14 | Project Presentations |
| Prerequisite(s): | Financial Econometrics I |
| Textbook(s): | Wooldridge J.M.(2015). Introduction to Econometrics. Cengage Learning Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: methods and applications. Cambridge university press. Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist's companion. Princeton university press. Cunningham, S. (2021). Causal inference: The mixtape. Yale university press. Gaillac, C., & L’Hour, J. (2019). Machine learning for econometrics, lecture notes ensae paris. |
| Additional Literature: | Lectures, Practical Sessions, Exercises |
| Laboratory Work: | Yes |
| Computer Usage: | Yes (Stata and R Software) |
| Others: | No |
|
COURSE LEARNING OUTCOMES
|
| 1 | Develop proficiency in advanced econometric techniques and methodologies, spanning topics such as binary dependent variables, measurement error, instrumental variables, panel data analysis, difference-in-differences methods, simultaneous equations, limited dependent variable models, multinomial variable models, regression discontinuity design, and resampling methods. |
| 2 | Cultivate the ability to critically analyze econometric models and methodologies, identify potential sources of bias or error, and implement appropriate strategies to address them. |
| 3 | Gain hands-on experience with econometric software and tools, such as STATA and R, in conducting empirical analysis, interpreting results, and drawing meaningful conclusions from economic and financial data. |
| 4 | Develop the skills necessary to design and execute empirical research studies in economics and related fields, including formulating research questions, selecting appropriate econometric methods, collecting and analyzing data, and communicating findings effectively. |
| 5 | Deepen understanding of causal inference methods, including quasi-experimental designs and identification strategies, to estimate causal effects in observational data settings and mitigate biases inherent in econometric analysis. |
| 6 | Recognize the interdisciplinary nature of econometrics and its applications across various domains, including economics, finance, public policy, and social sciences, and appreciate the broader implications of econometric analysis for decision-making and policy formulation. |
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COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES
(Blank : no contribution, 1: least contribution ... 5: highest contribution) |
| No | Program Competencies | Cont. |
| Bachelor in Banking and Finance (3 years) Program | ||
| 1 | The students gain the ability to look at the problems of daily life from a broader perspective with an increased awareness of the importance of moral/ethical considerations and professional integrity in the workplace. | 4 |
| 2 | They develop their knowledge and understanding of banking and finance including concepts, theories, and analytical tools that serve both in national and international markets. | 5 |
| 3 | They gain an understanding of the role of financial management in business firms and the essentials of corporate finance and further develop their knowledge in the field. | 3 |
| 4 | They are able to apply valuation models to estimate the price of different financial assets, measure risk and describe the risk-return tradeoff. | 4 |
| 5 | They are provided with the knowledge and understanding of the regulatory framework and functioning of banking system and central banking as well as international banking system. | 3 |
| 6 | They are able to understand and use fundamental economic theories and tools to solve economic problems in banking and financial services industry. | 5 |
| 7 | They have the ability to develop and utilize accounting, financial and economic data as well as other information to solve different business problems by making use of basic mathematical and statistical models. | 5 |
| 8 | They are expected to develop their numerical and IT skills as well as knowledge of databases in order to address the significant development in the delivery and use of financial services known as FinTech. | 5 |
| 9 | They develop their ability to think critically, do research, analyze, interpret, draw independent conclusions, and communicate effectively, both individually and as part of a team. | 5 |
| 10 | They are provided with opportunities to acquire the necessary skills and competencies to develop professionalism in the banking and financial services industry or to move on to further study within the discipline. | 5 |
|
COURSE EVALUATION METHOD
|
| Method | Quantity | Percentage |
| Homework |
2
|
5
|
| Presentation |
2
|
10
|
| Project |
2
|
5
|
| Term Paper |
1
|
30
|
| Final Exam |
1
|
30
|
| Total Percent: | 100% |
|
ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)
|
| Activities | Quantity | Duration(Hours) | Total Workload(Hours) |
| Course Duration (Including the exam week: 16x Total course hours) | 16 | 3 | 48 |
| Hours for off-the-classroom study (Pre-study, practice) | 16 | 3 | 48 |
| Mid-terms | 1 | 16 | 16 |
| Assignments | 1 | 10 | 10 |
| Final examination | 1 | 18 | 18 |
| Other | 1 | 10 | 10 |
|
Total Work Load:
|
150 | ||
|
Total Work Load/25(h):
|
6 | ||
|
ECTS Credit of the Course:
|
6 | ||
|
CONCLUDING REMARKS BY THE COURSE LECTURER
|
|
Each week a randomly picked student has to prepare a presentation on the reading list they will find on Google Classroom. Each person will need to present once. In addition to the short presentations, each paper will be assigned to a student to write and present a “referee report”. The aim of the referee report is to be critical: Give a short overview of the paper; think about an alternative explanation/story, and criticize the empirical identification strategy of the paper (and try to suggest an alternative one). |