ECON 3101 - Econometric Analysis I

Basic Information

Course Code:

ECON 3101

Course Name:

Econometric Analysis I

Language of Instruction:

English

Course Type:

Class

Course Level:

Bachelor

ECTS:

5.00

Instructor:

Dr. Öğr. Üyesi FIRAT YILMAZ

Course Objectives

The aim of this course is to enable students to understand the fundamental concepts and methods of econometrics and to analyze economic relationships empirically. The course covers simple and multiple linear regression models, the Ordinary Least Squares method, interpretation of regression coefficients, hypothesis testing, confidence intervals, and evaluation of model fit. Students are expected to construct econometric models using economic data, estimate models in R/RStudio, and interpret the resulting findings from an economic perspective.

Course Objectives

The aim of this course is to enable students to understand the fundamental concepts and methods of econometrics and to analyze economic relationships empirically. The course covers simple and multiple linear regression models, the Ordinary Least Squares method, interpretation of regression coefficients, hypothesis testing, confidence intervals, and evaluation of model fit. Students are expected to construct econometric models using economic data, estimate models in R/RStudio, and interpret the resulting findings from an economic perspective.

Course Content

Introduction to econometrics and the econometric modelling process; relationship between economic theory, mathematical models, and econometric models; types of economic data; simple linear regression; Ordinary Least Squares estimation; estimation and interpretation of regression coefficients; basic assumptions of the classical linear regression model; properties of estimators; goodness of fit and R-squared; statistical inference; t and F tests; confidence intervals; multiple linear regression; inclusion and exclusion of explanatory variables; functional forms; logarithmic models; introduction to dummy variables; preparation of economic datasets, estimation of regression models, and interpretation of results using R/RStudio.

Prerequisites / Corequisites

None

Course Books / Materials / Recommended Resources

Main Textbook:Heiss, F., Using R for Introductory Econometrics, CreateSpace Independent Publishing Platform.Recommended Resources:Wooldridge, J. M., Introductory Econometrics: A Modern Approach, Cengage Learning.Hanck, C., Arnold, M., Gerber, A. & Schmelzer, M., Introduction to Econometrics with R.Kleiber, C. & Zeileis, A., Applied Econometrics with R, Springer.Gujarati, D. N. & Porter, D. C., Basic Econometrics, McGraw-Hill.R and RStudio software, course notes, economic datasets, and R application files.

Learning Outcomes

Code	Description
LO1	Ability to construct econometric models and define model variables
LO2	Ability to prepare data, estimate models, and generate outputs in R
LO3	Ability to interpret regression coefficients, significance tests, and confidence intervals
LO4	Ability to apply model assumptions and diagnostic tests and evaluate their results
LO5	Ability to present empirical findings in the form of tables, graphs, and concise reports

Weekly Course Content

Week	Content
1	Introduction to econometrics; objectives of econometrics, relationship between economic theory and econometric models, and the econometric research process
2	Types of economic data; cross-sectional, time-series, and panel data; introduction to R/RStudio and importing data
3	Simple linear regression model; defining dependent and independent variables and interpreting the regression line
4	Ordinary Least Squares (OLS); estimation of regression coefficients and simple regression applications in R
5	Economic and statistical interpretation of regression coefficients; goodness of fit and R-squared
6	Basic assumptions of the classical linear regression model and properties of OLS estimators
7	Statistical inference; standard errors, t tests, hypothesis testing, and confidence intervals
8	Midterm Examination
9	Introduction to multiple linear regression; incorporating multiple explanatory variables into the model
10	Interpretation of multiple regression coefficients; partial effects and ceteris paribus interpretation
11	Hypothesis testing in multiple regression; individual t tests and joint significance using the F test
12	Evaluation of model fit; R-squared, adjusted R-squared, and model comparison
13	Functional forms; logarithmic, linear-log, and log-linear models
14	Introduction to dummy variables; use and interpretation of categorical variables in regression models
15	Integrated econometric application using R/RStudio; data preparation, model estimation, presentation of results with tables and graphs, and overall review

Workload Calculation

Activity	Count	Duration (Hours)	Total
Attendance	15	4.00	60.00
Post-Class Individual Study	15	3.00	45.00
Midterm Exam/Preparation	15	1.00	15.00
Final Exam/Preparation	30	1.00	30.00
Total Workload (Hours)			150
ECTS Credit (Workload / 25)			5

Assessment

#	Assessment Type	Contribution (%)
1	Midterm Exam	%40
2	Final Exam	%60
TOTAL		%100

PO - LO Matrix

PO \ LO	LO1	LO2	LO3	LO4	LO5
PO-1
PO-2
PO-3
PO-4
PO-5
PO-6
PO-7
PO-8
PO-9
PO-10
PO-11
PO-12

1

Low Contribution

2

Medium Contribution

3

High Contribution

Teaching and Learning Methods

#	Method Name	Description	Tools
1	Lecture (expository teaching), interactive discussion	Listening and taking notes.	Standard classroom technologies, multimedia tools (projector, computer, digital presentations)

Academic Integrity and Artificial Intelligence

Students are expected to comply with the principles of academic integrity in all assignments, projects, examinations, and other academic work. Information, data, tables, figures, code, and ideas obtained from external sources must be properly acknowledged. Plagiarism, manipulation of data or results, completing work on behalf of another student, and unauthorized collaboration are considered violations of academic integrity.Artificial intelligence tools may be used, when permitted by the course instructor, to support learning, clarify econometric concepts, assist with R coding, or explore analytical methods. However, students remain responsible for verifying the accuracy of AI-generated code, analyses, and interpretations. AI tools may not replace students' own econometric analysis, interpretation, or academic responsibility. Any use of artificial intelligence must comply with the instructor's guidelines and should be disclosed when required.

Sustainable Development Goals