Econometrics I (ECON301) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Econometrics I ECON301 5. Semester 3 0 0 3 6
Pre-requisite Course(s)
N/A
Course Language English
Course Type Compulsory Departmental Courses
Course Level Bachelor’s Degree (First Cycle)
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Demonstration.
Course Coordinator
Course Lecturer(s)
  • Asst. Prof. Dr. Nil Demet Güngör
Course Assistants
Course Objectives The aim of this course is to introduce students to the study of econometrics, which deals with the application of statistical methods to test economic theory. Econometrics uses observational data to estimate economic relationships, test hypotheses about economic behaviour, and predict future values of economic variables. Software applications are introduced during the course in order to provide hands-on experience with data collection, analysis and interpretation.
Course Learning Outcomes The students who succeeded in this course;
  • Distinguish between different types of data used in econometric analysis
  • Understand the use of econometric methods in estimating causal relationships and building models in economics and related fields
  • Estimate and interpret the results of empirical models
  • Use econometric software in simple applications
Course Content Review of basic statistics; simple regression, tests of hypothesis; prediction; assessing goodness of fit; assumptions of the classical linear regression model; transformation of variables; estimation and inference in the multiple regression model.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Review of Basic Statistics - Descriptive Statistics, Probability and Random variables; Introduction – The Methodology of Economics Gujarati, Introduction: pp. 1-13
2 The Nature of Regression Analysis – Causation, Correlation and Types of Data Gujarati, Chapter 1: pp. 15-32
3 Two Variable Regression Model: Some Basic Ideas Gujarati, Chapter 2: ss. 37-52
4 Two Variable Regression Model: The Problem of Estimation Gujarati, Chapter 3: pp. 58-105
5 Two Variable Regression Model: The Problem of Estimation Gujarati, Chapter 3: pp. 58-105
6 The Normality Assumption: Classical Normal Linear Regression Model Gujarati, Chapter 4: pp. 107-113
7 Two-Variable Regression Model: Interval Estimation and Hypothesis Testing Gujarati, Chapter 5: pp. 119-133
8 Two-Variable Regression Model: Interval Estimation and Hypothesis Testing Gujarati, Chapter 5: pp. 134-150
9 MIDTERM EXAM
10 Introduction to Eviews Class Handouts
11 Extensions of the Two-Variable Regression Model: Scaling, Functional Forms Gujarati, Chapter 6: pp. 164-193
12 Multiple Regression Model: The Problem of Estimation Gujarati, Chapter 7: pp. 202-232
13 Multiple Regression Model: The Problem of Inference Gujarati, Chapter 8: pp. 248-263
14 Multiple Regression Model: The Problem of Inference Gujarati, Chapter 8: pp. 264-280
15 General Review
16 Final Exam

Sources

Course Book 1. Gujarati, Damodar N. (2003) Basic Econometrics, 4th Edition, New York and Boston: McGraw-Hill.
Other Sources 2. Wooldridge, Jeffrey (2008) Introductory Econometrics: A Modern Approach (with Economic Applications), 4th Edition, Cengage Learning.
3. Peter J. Kennedy (1998) A Guide to Econometrics, 4th Edition, MIT Press.
4. Ramanathan, R. (2002), Introductory Econometrics with Applications, 5th edition, Orlando, FL: Harcourt College Publishers.
5. Hill, R.C., Griffiths, W.E. and G. G. Judge (2001) Undergraduate Econometrics, 2nd Edition, John Wiley and Sons, Inc.
6. Hill, R.C., Griffiths, W.E. and G. G. Judge (2000) Using Eviews For Undergraduate Econometrics, 2nd Edition, Wiley.
7. Asteriou, D. (2006) Applied Econometrics: A Modern Approach using EViews and Microfit, Palgrave-Macmillan.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation 1 10
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 3 15
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 30
Final Exam/Final Jury 1 45
Toplam 6 100
Percentage of Semester Work 55
Percentage of Final Work 45
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Acquiring the skills of understanding, explaining, and using the fundamental concepts and methods of economics X
2 Acquiring the skills of macro level economic analysis X
3 Acquiring the skills of micro level economic analysis X
4 Understanding the formulation and implementation of economic policies at the local, national, regional, and/or global level X
5 Learning different approaches on economic and related issues X
6 Acquiring the quantitative and/or qualitative techniques in economic analysis X
7 Improving the ability to use the modern software, hardware and/or technological devices X
8 Developing intra-disciplinary and inter-disciplinary team work skills X
9 Acquiring an open-minded behavior through encouraging critical analysis, discussions, and/or life-long learning X
10 Adopting work ethic and social responsibility X
11 Developing the skills of communication. X
12 Improving the ability to effectively implement the knowledge and skills in at least one of the following areas: economic policy, public policy, international economic relations, industrial relations, monetary and financial affairs. X

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 6 96
Presentation/Seminar Prepration
Project
Report
Homework Assignments 2 2 4
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury
Prepration of Final Exams/Final Jury
Total Workload 148