COURSE INFORMATION Course 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)	Dr. Fatbardha Morina fmorina@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 , Thursday 12:00-15:00
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:	Monday and Thursday
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:	To provide students with the skills required to research in economics and finance using more advanced econometric practices and models. Real Life applications are analyzed via the STATA econometric package. Laboratory sessions help students to gain knowledge and new skills in demonstrating and interpreting the results of various static and dynamic models.

BASIC CONCEPTS OF THE COURSE

1	Binary Dependent Variable: A variable that takes on only two possible outcomes (e.g., yes/no, employed/unemployed).
2	Proxy Variables and Measurement Error: Proxy variables substitute for unobserved or imperfectly measured variables, while measurement error refers to inaccuracies in data that can bias estimates.
3	Instrumental Variables (IV): Variables used to obtain consistent parameter estimates when explanatory variables are endogenous, often due to measurement error or omitted factors.
4	Panel Data: Data that follows the same subjects over multiple time periods, allowing for analysis of both cross-sectional and time-series variations.
5	Difference-in-Differences (DiD): A quasi-experimental design that compares changes in outcomes over time between treatment and control groups to estimate causal effects.
6	Generalized Method of Moments (GMM): An estimation technique that uses moment conditions derived from theoretical models to efficiently and consistently estimate parameters.
7	Simultaneous Equations: A system where multiple interdependent relationships are modeled together, requiring specialized methods to address the mutual determination of variables.
8	Limited Dependent Variable Models: Models designed for outcomes that are restricted in range (such as binary or censored variables), often implemented using techniques like Logit or Probit.
9	Multinomial Variable Models: Models used when the dependent variable has more than two unordered categories, such as choices among several alternatives.
10	Regression Discontinuity Design (RDD): A quasi-experimental approach that identifies causal effects by exploiting a cutoff or threshold in the assignment of treatment.

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	Generalized Method of Moments (GMM), a robust econometric technique that leverages moment conditions derived from economic theory. Students will learn to construct and validate these moment conditions, select optimal weighting matrices, and assess estimator efficiency. The course covers the asymptotic properties of GMM estimators and includes hands-on sessions using statistical software.
8	Midterm
9	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.
10	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
11	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.
12	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.
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	To have skills to set up robust parsimonious econometric models.
2	To have the ability of testing specification of the model.
3	To have the required skills to analyze financial time series and related regressions.
4	To model multivariate relationships using either dynamic or static form.

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.
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.
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.
4	They are able to apply valuation models to estimate the price of different financial assets, measure risk and describe the risk-return tradeoff.
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.
6	They are able to understand and use fundamental economic theories and tools to solve economic problems in banking and financial services industry.
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.
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.
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.
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.

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Homework	2	10
Presentation	1	10
Case Study	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

If a student has a misbehavior report then automatically that student gets zero points for that exam. The same rule goes if the projects they submit have high plagiarism