COURSE INFORMATION Course Title: ECONOMETRICS I

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
ECO 311	B	5	2	0	2	3	5

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 , Monday 9:00 AM
Second Course Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	NA
Language:	English
Compulsory/Elective:	Compulsory
Study program: (the study for which this course is offered)	Bachelor in Business Administration (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:	Econometrics I: Techniques of Econometrics, estimating the basic linear model and hypothesis testing, empirical illustrations to contemporary economic issues
Course Objectives:	The goal of this course is to provide students with knowledge of the elements of statistical inference, namely multivariate statistics and multivariate data analysis methods. Students will understand and be able to perform standard descriptive and inferential data analysis, investigate and test relationship between variables as well as specify, use and interpret multivariate models, including regression-type models. The course will also emphasize empirical analysis and focus on the use of data in practice along with the use of available statistical software. An empirical project is an integral part of the course. If possible, economics, financial, and business applications will be chosen during the course to reflect the interests and backgrounds of students.

BASIC CONCEPTS OF THE COURSE

1	Ordinary least squares
2	Regression
3	Panel data
4	Instrumental Variable
5	Model Specification
6	Multicollinearity
7	Heteroskedasticity
8	Autocorrelation
9	Time Series
10	Endogeneity

COURSE OUTLINE

Week	Topics
1	Intro; Evaluation Method; Term Project & A brief Lecture on Types of Data
2	Descriptive Summary, Chapter 1 page: 1-30. Data Sources and Graphical Representation of Data 2. Summary Statistics for one Variable 3. Summary Statistics for two (or more) variables
3	Regression Analysis, Chapter 1 page: 1-30 1. What is regression analysis? 2. The Classical Model: Ordinary Least Squares (OLS) 3. Learning and Using Regression Analysis/Running Your Own Project 4. Practical issues: Reading Computer Output 5. The Classical Model: Assumptions and Properties 6. Hypothesis Testing
4	Ordinary Least Squares, Chapter 2 page: 35-63 1. What is regression analysis? 2. The Classical Model: Ordinary Least Squares (OLS) 3. Learning and Using Regression Analysis/Running Your Own Project 4. Practical issues: Reading Computer Output 5. The Classical Model: Assumptions and Properties 6. Hypothesis Testing
5	Assymptotic theory/properties and testing in regression, Chapter 3, page:65-89 1. What is regression analysis? 2. The Classical Model: Ordinary Least Squares (OLS) 3. Learning and Using Regression Analysis/Running Your Own Project 4. Practical issues: Reading Computer Output 5. The Classical Model: Assumptions and Properties 6. Hypothesis Testing
6	Model specification, Chapter 4, page:92-108 1. Choosing the Variables in a Regression 2. Including and Interpreting Categorical Variables 3. Choosing the Functional Form
7	Model specification and closing functional form, Chapter 4, page:92-108 1. Choosing the Variables in a Regression 2. Including and Interpreting Categorical Variables 3. Choosing the Functional Form
8	How to write a research paper and review for midterm, Chapter 11, page:340-358 1. Choosing a Research Project 2. Data Management
9	Midterm
10	Heteroskedasticity, Chapter 8-10, page: 221-337 1. Outliers 2. Multicollinearity 3. Heteroskedasticity and Autocorrelation 4. Lagged Dependent Variables and Time Series
11	Autocorrelation and Lagged dependent variable (Time Series), Chapter 12, page:364-385 1. Outliers 2. Multicollinearity 3. Heteroskedasticity and Autocorrelation 4. Lagged Dependent Variables and Time Series
12	Transformations, Endogeneity and Instrumental Variables, Chapter 16, page 465-484: Miscellaneous (some topics could be replaced/extended base on the class response) 1. Discontinuity design, diff-in-diff 2. Sensitivity issues, using robustness approach in the analysis 3. Introduction to non-parametric methods 4. Collecting data – introduction to survey data
13	Project Presentations
14	Project Presentations + Review

Prerequisite(s):	Statistics I and II
Textbook(s):	Studenmund (2011): Using Econometrics: A Practical Guide, 6th edition, Pearson. The website for the book (www.pearsonhighered.com/studenmund) includes the datasets mentioned in the book formatted for use in Stata (and other programs). It also includes additional interactive regression learning exercises.
Additional Literature:	Wooldridge, J.M. (2003) Introductory Econometrics: A Modern Approach, 2nd ed, Thomson/South-Western. Bruce Hansen (2022), Econometrics
Laboratory Work:	yes
Computer Usage:	yes
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.
5	Understand how to specify and estimate econometric models, including simple linear regression, multiple regression, and time series models, in the context of economic data.
6	Gain proficiency in hypothesis testing, including testing the significance of coefficients, assessing model fit, and conducting various statistical tests such as t-tests, F-tests, and chi-squared tests.
7	Learn how to use econometric models for predictive purposes, including forecasting economic variables and evaluating model performance.
8	Understand the challenges and methods associated with making causal inferences from observational data, including issues related to endogeneity, omitted variables, and instrumental variables.
9	Gain hands-on experience with econometric software packages (e.g., R, Python, Stata) to perform data analysis and estimate econometric models.
10	Cultivate critical thinking skills by evaluating the strengths and limitations of econometric models and their applicability to real-world economic problems.

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

No	Program Competencies	Cont.
Bachelor in Business Administration (3 years) Program
1	Identify activities, tasks, and skills in management, marketing, accounting, finance, and economics.	3
2	Apply key theories to practical problems within the global business context.	4
3	Demonstrate ethical, social, and legal responsibilities in organizations.	2
4	Develop an open minded-attitude through continuous learning and team-work.	3
5	Use technology to enable business growth and sustainability.	1
6	Analyze data to make effective decisions.	3

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Homework	4	5
Midterm Exam(s)	1	25
Project	1	25
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	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

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