COURSE INFORMATION Course Title: ECONOMETRICS II

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
ECO 312	B	6	4	0	0	4	6

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 , Saturdays 9:45 - 13:45
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:	Elective
Study program: (the study for which this course is offered)	Bachelor in Economics (3 years)
Classroom and Meeting Time:	Saturdays _ Computer LAB III
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 to enter the final examination of the course.
Course Description:	Theory and Economic application f the linear multiple regression model, identification and structural estimation in simultaneous models, analyzing of economic policy and forecasting.
Course Objectives:	To provide students with the skill set required to carry out research in banking, economics and finance using advanced econometric practices and models. Real Life applications are analyzed via the E-VIEWS econometric package. Laboratory sessions help students to gain knowledge and advance critical thinking skills in estimating and interpreting the results of various static and dynamic models built on time series and panel data.

BASIC CONCEPTS OF THE COURSE

1	Time Series Data
2	Panel Data, Unbalanced vs Balanced data sets
3	Static vs Dynamic Models
4	ARCH, GARCH, E-GARCH
5	Fixed Effects Model, Random Effects Model, Generalized Method of Moments
6	Serial Correlation
7	Dynamically Complete Models
8	Stationarity vs Nonstationarity

COURSE OUTLINE

Week	Topics
1	Introduction to the: Course, Syllabus, Textbook(s) and Evaluation Method.
2	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).
3	Chapter 9 "More on Specification and Data Issues". This chapter firstly intends to present the audience to the consequences of functional form misspecification (304-306). When does it arise? Which are the causes? How to detect the problem and lastly how to tackle the problem? - pg 307-331. Exercises and Applications to be discussed and solved during the Lab Session (pg 335-338). Main Reference: Wooldridge - Cengage Learning.
4	Chapter 10 "Basic Regression Analysis with Time Series Data" (pg 344). Static and FDL models (pg 345-349). Properties of OLS under the full set of CLM assumptions (pg 349-355). Dummies, trends and seasonality (356-371). Applications - pg 375-380.
5	Review before the 1st application exam. Discussion on the individual term projects.
6	Application Exam Nr:1 (Computer-Based)
7	Midterm (Paper - Based)
8	Chapter 11 "Further Issues in Using OLS with Time Series Data" - pg 380. Stationary and non-stationary time series, notes of caution on the uses of time series data, various transformations applicable with the stated data (pg 381-395). Properties of OLS, introduction to serial correlation problem, dynamically complete models (pg 396-402). Applications (pg 404-410).
9	Chapter 12 "Serial Correlation and Heteroskedasticity in Time Series Regressions" (pg 412). Discussion on the concepts of consistency, efficiency of OLS, unbiasedness (pg 412-416). Testing for serial correlation, testing for heteroscedasticity, solving the problems that arise with time series estimation, FGLS (pg 416 - 438). End of chapter applications (440-445).
10	Introduction to advanced models with panel data: estimation & interpretation of results. On key focus will be the explanation of main features of POLS, FEM, REM, GMM and differences between them. Reference: Wiley (Advanced Time Series) & Cengage ( Econometrics _ A modern approach)
11	Introduction to advanced models with time series: ARDL, VAR, VECM, Cointegration, ARCH, GARCH. Estimation method, Interpretation of results, matters of caution. The lecture will end with a discussion of papers which employ such models in examination of financial and economic phenomena.
12	Individual term project presentations.
13	Application Exam Nr:2 (Computer-Based)
14	Review for Final Exam

Prerequisite(s):	Financial Econometrics I, Statistics I and II, Mathematics.
Textbook(s):	Econometrics - A modern Approach: Wooldridge J.M. (2016 & 2019) , Cengage Learning.
Additional Literature:	Advanced Time Series Data Analysis (2019), Wiley. Analysis of Financial Time Series (2010), Wiley. Lectures, Practical Sessions, Exercises, Journal Articles & Published Papers.
Laboratory Work:	Yes
Computer Usage:	Yes (E-views XI Software)
Others:	No

COURSE LEARNING OUTCOMES

1	To have skills to set up robust parsimonious econometric models.
2	To have the ability of testing the 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.
5	To use advanced time series or panel data models to explain economic, financial, and social phenomena both theoretically and empirically.

COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES (Blank : no contribution, 1: least contribution ... 5: highest contribution)

No	Program Competencies	Cont.
Bachelor in Economics (3 years) Program
1	Students define the fundamental problems of economics	3
2	Students describe key economic theories	4
3	Students critically discuss current developments in economics	4
4	Students appropriately use software for data analysis	5
5	Students critically contextualize the selection of an economic problem for research within scholarly literature and theory on the topic	5
6	Students apply appropriate analytical methods to address economic problems	5
7	Students use effective communication skills in a variety of academic and professional contexts	5
8	Students effectively contribute to group work	5
9	Students conduct independent research under academic supervision	5
10	Students uphold ethical values in data collection, interpretation, and dissemination	5
11	Students critically engage with interdisciplinary innovations in social sciences	4
12	Student explain how their research has a broader social benefit	5

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Midterm Exam(s)	1	25
Project	1	15
Quiz	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	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

NA