COURSE INFORMATION Course Title: FINANCIAL ECONOMETRICS I

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
BAF 333	B	5	4	0	0	4	5

Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature)	NA
Main Course Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	M.Sc. Egis Zaimaj ezaimaj@epoka.edu.al , Wednesday 9:15 - 11:30
Second Course Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	NA
Teaching Assistant(s) and Office Hours:	NA
Language:	English
Compulsory/Elective:	Compulsory
Study program: (the study for which this course is offered)	Bachelor in Banking and Finance (3 years)
Classroom and Meeting Time:	Computer Lab III & Computer Lab I
Code of Ethics:	Code of Ethics of EPOKA University Regulation of EPOKA University "On Student Discipline"
Attendance Requirement:	A minimum attendance rate of 60% is required for the student to enter the examination.
Course Description:	BAF 333 - Financial Econometrics I: Techniques of Econometrics, estimating the basic linear model and hypothesis testing, empirical illustrations to contemporary economic issues. The objective of this course is to prepare students for basic empirical work in economics and finance. In particular, topics will include basic data analysis, regression analysis, testing, and forecasting. Students will be provided with the opportunity to use actual economic data to test economic theories.
Course Objectives:	The primary objective of this course is to teach students fundamental econometric techniques in a highly empirical but theoretically rigorous context. This course presents an applied introduction to econometric techniques with some derivations of their properties, but leaves more theoretical treatment to future courses. For all groups, the course provides practical experience in the use of econometric software EVIEWS.

BASIC CONCEPTS OF THE COURSE

1	Classical Linear Model Assumptions
2	Ordinary Least Squares
3	Weighted Least Squares, Feasible Least Squares
4	Linear Probability Model
5	Interaction Terms
6	Quadratic Terms
7	Turning Point
8	Hypothesis Testing
9	Prediction Intervals
10	Goodness of Fit

COURSE OUTLINE

Week	Topics
1	Introductory Lecture, Term Project, Evaluation Method, Reference Materials.
2	Chapter 1 “Types and Nature of Economic Data”: This chapter lays the foundation for the theoretical and empirical analysis that follows. Firstly, the students are presented to econometrics as a discipline that builds on advanced mathematics, advanced statistics and advanced economics. The main contributions of such a discipline are put forward to the students, together with several practical examples (pg 1-2, Wooldridge). Secondly, students are presented to data classification. What are the main categories of economic data and which are the key distinctive characteristics of each? (pg 5-11, Wooldridge and pg 9-14, Analysis of Economic Data). The chapter ends with a discussion on the notion “ceteris paribus” and “causality” (pg 12-16).
3	Chapter 2 “Simple Linear Regression Models”: During this week, we take firstly some time to review key statistical concepts such as: correlation, measures of central tendency, shape of the distribution, graphical analysis (pg 35-46, Analysis of Economic Data). Then, we start with the basic case of the simple linear regression model; presenting the theory behind the OLS estimation method and then several practical examples (pg 22-35, Wooldridge and pg 49-50, Analysis of Economic Data). Thirdly, we go in depth of concepts such as: unbiasedness, algebraic properties of OLS statistics, nonlinearitites and alike (pg 35-57, Wooldridge).
4	Chapter 3 “Multiple Linear Regression Models - Estimation and Interpretation”: The chapter begins by presenting the “strengths” of multiple linear regression models over simple linear regression models. It shows several advantages of the former and how it contributes directly to researchers’ objective of added accuracy and prediction power (pg 69-71, Wooldridge). Next, we show how OLS estimates can be obtained, tested and interpreted. Students are presented with numerous estimation outputs so as to related theory to practical examples (pg 72-74). What follows is a lengthy discussion on: Gauss Markov Assumptions, Omitted Variable Bias & Irrelevant Variable Bias, X or Y transformations and consequences of such procedures, The Variance of the OLS Estimators (pg 76-101).
5	Chapter 4 “Further concepts on Multiple Linear Regression Models”: Having had a complete introduction to multiple linear regression models, students are now presented to several procedures that fall under inferential statistics: t-test (one-sided vs two-sided hypothesis testing; pg 123-130 Wooldridge), P-value (pg 133), confidence intervals (pg 138), F-test (exclusion restrictions; pg 143 Wooldridge). In this chapter the student is taught to focus on key elements of the estimation output such as: beta coefficients, P values, the coefficient of determination, P (F-statistic), and give a detailed interpretation of the estimation output (pg 154). The idea is also to identify the limitations in each case and derive inferences that serve the interested parties.
6	Chapter 5 “OLS Estimation”: This chapter presents the different obstacles that researchers might face while conducting an empirical analysis which might then have a negative impact on the accuracy and reliability of the results. Key concepts discussed in this chapter are consistency of OLS (pg 169-172, Wooldridge); Asymptotic Normality and Large Sample Inference (pg 173, Wooldridge); The Lagrange Multiplier Statistic (pg 178, Wooldridge); Asymptotic Efficiency of OLS (pg 181, Wooldridge).
7	Application Nr:1 (Computer-Based)
8	Midterm (Paper - Based)
9	Chapter 6 “Multiple Regression Analysis - Further Issues”: The chapter begins by discussing the complexity and diversity of economic interrelations between groups of variables. There are cases when the impact of one variable on the response variable changes depending on the initial value that this explanatory variable takes on; in other cases, it depends on the value of yet another explanatory variable. There are also cases in which we have to rescale either the dependent or the independent variables. Firstly, the chapter focuses on data scaling and the effects that it brings for OLS statistics (pg 186-189, Wooldridge). Then, the discussion focuses on the importance of functional form; quadratic terms, interaction terms as well as logarithmic transformation (pg 191-198). By the end of the chapter is discussed the following: nested and non-nested models, adjusted R-squared, error variance, prediction and residual analysis (pg 200-212).
10	Chapter 7 “Linear Probability Models”: What regression models generally incorporate and capture is the impact of one quantitative (or more) variable(s) on another quantitative variable. Yet, there are cases that researchers need to account for qualitative information. They can do so by relying on dummy variables or categorical variables (pg 227, Wooldridge). At first, the chapter presents the concept of including a single dummy variable in the regression equation (pg 228); then, it focuses on a more inclusive case: that of using dummy variables for multiple categories (pg 235). What follows is a discussion on the Linear Probability Model and its uses (pg 248). To end the chapter, a discussion on interpreting regression results with discrete dependent variables is included, coupled with several practical examples from literature (pg 256).
11	Chapter 8 “Heteroscedasticity - Causes and Remedies”: This chapter builds on four main pillars: causes of heteroscedasticity; tests for heteroscedasticity; consequences of heteroscedasticity and lastly, remedies (ways in which we can tackle such a problem). To draw students’ attention to this serious problem, the chapter begins with a detailed discussion on consequences of heteroskedasticity for OLS (pg 268). Then, some of the most popular heteroscedasticity tests are presented to the students. Here, we can mention White Test or Breusch Pagan Test (pg 275-279). What follows is a discussion on GLS (generalized least squares) and Weighted Least Squares Estimation (pg 280-286).
12	Individual Project Presentations
13	Application Nr:2 (Computer-Based)
14	Revision prior to the Final Exam

Prerequisite(s):	na
Textbook(s):	Introduction to Econometrics, J.M. Wooldridge, Cengage Learning. (7th Edition, 2020) & Analysis of Economic Data. Gary Koop, Second Edition.
Additional Literature:	Journal Articles, Conference Papers.
Laboratory Work:	yes
Computer Usage:	yes (E-Views 10 Software)
Others:	No

COURSE LEARNING OUTCOMES

1	The student should be able to understand the nature of Econometrics and apply the basic econometric techniques in different studies.
2	The student should be able to estimate and interpret econometric models.
3	The student should be able to check the robustness and specification of the econometric models.
4	The student should be able to apply fundamental econometric principles to real life and scientific problems as well as test economic theories.

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.	5
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.	4
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.	5
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.	4
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.	4
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.	4

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Midterm Exam(s)	1	25
Project	1	15
Lab/Practical Exams(s)	2	10
Final Exam	1	30
Other	1	10
	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	4	64
Hours for off-the-classroom study (Pre-study, practice)	16	2	32
Mid-terms	1	6	6
Assignments			0
Final examination	1	10	10
Other	1	13	13
Total Work Load:			125
Total Work Load/25(h):			5
ECTS Credit of the Course:			5

CONCLUDING REMARKS BY THE COURSE LECTURER

NA