Econometrics II (ECON302) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Econometrics II ECON302 6. 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 This course is a continuation of ECON 301, which set out the basic assumptions of the classical linear regression model (CLRM). The assumptions of the CLRM are usually not satisfied in econometric applications. This course will look at: the detection and consequences of violations of the CLRM including multicollinearity, heteroskedasticity, autocorrelation, and model misspecification, as well as a selection of further topics in econometrics including model specification, diagnostic testing. Applications to real world data are emphasized to illustrate the concepts introduced in the course
Course Learning Outcomes The students who succeeded in this course;
  • Understand and apply more advanced econometric techniques
  • Appreciate the advantages and limitations of an econometric method in various situations
  • Demonstrate practical skills in the application of computer software to econometric modelling
  • Interpret empirical results and critically assess studies that use econometric techniques
  • Gain confidence in writing empirical research reports
Course Content Review of regression and hypothesis testing; dummy variable regression models; multicollinearity; heteroskedasticity; autocorrelation; model misspecification; model selection criteria; outlier analysis.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Review of material learned in ECON 301 Class Handouts
2 Dummy Variable Regression Models Gujarati, Chapter 9: pp. 297-309
3 Special Applications of Dummy Variables Gujarati, Chapter 9: pp. 310-323
4 Nature and Consequences of Multicollinearity Gujarati, Chapter 10: pp. 335-358
5 Multicollinearity: Detection and Remedial Measures Gujarati, Chapter 10: pp. 359-375
6 EViews Applications Class Handouts
7 MIDTERM EXAM
8 Nature and Consequences of Heteroskedasticity Gujarati, Chapter 11: pp. 387-400
9 Heteroskedasticity: Detection and Remedial Measures Gujarati, Chapter 11: pp. 400-428
10 Nature and Consequences of Autocorrelation Gujarati, Chapter 12: pp. 441-461
11 Autocorrelation: Detection and Remedial Measures Gujarati, Chapter 12: pp. 462-489
12 Econometric Modeling: Model Misspecification, Model Selection Criteria Gujarati, Chapter 13: pp. 506-529
13 Econometric Modeling: Diagnostic Testing and Outlier Analysis Gujarati, Chapter 13: pp. 530-547
14 EViews Applications Class Handouts
15 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 - -
Presentation - -
Project 1 20
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 30
Final Exam/Final Jury 1 30
Toplam 4 90
Percentage of Semester Work 70
Percentage of Final Work 30
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
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 2 2
Prepration of Final Exams/Final Jury 1 2 2
Total Workload 148