Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
2ECON 508Econometrics3+0+038

 
Course Details
Language of Instruction English
Level of Course Unit Master's Degree
Department / Program MA Program in Economics (Thesis) (English)
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course The ultimate goal of this course is to equip students with analytical ability and pave them the way for exploring modern data mining tools.
1. To provide tools that help to learn the characteristics of economic data and their usage in economic modelling.
2. To provide the students with theoretical and intuitive understanding of different econometric models, their assumptions and inferences.
3. To illustrate the potential problems encountered in the model selection process and the solutions to these problems.
4. To equip how to select the appropriate model that fits best to the data and that produces the best results according to the robust criteria.
5. To familiarize the students, with different estimation procedures and their best usage in different scenarios.
Course Content Econometrics is a branch of economics. It applies mathematical and statistical methods to explore and quantify the relationships between economic, financial and social variables where these relationships are either hypothesized by models or based on observed phenomena. This course covers different estimation methodologies used in Econometrics and detailed discussion on Time Series Analysis. Time series models ranging from univariate models, single equation multivariate models and multiple equation models are discussed in detail.
Course Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Prof.Dr. Muhittin Kaplan
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources • [GME]: Marno Verbeek, A Guide to Modern Econometrics, 2nd Ed. John Wiley & Sons Ltd. (in IHU library)
• [EM]: Johnston ve J. DiNardo, Econometric Methods, McGraw-Hill
• [ETM]: Econometric Theory and Methods, Oxford University Press, 2003. (in IHU library)
• [TSA]: Time Series Analysis with Applications in R, Springer (in IHU library)
• [ATE]: Applied Time Series Econometrics, Cambridge University Press.
• [GME]: Marno Verbeek, A Guide to Modern Econometrics, 2nd Ed. John Wiley & Sons Ltd.
• [EM]: Johnston ve J. DiNardo, Econometric Methods, McGraw-Hill
• [ETM]: Econometric Theory and Methods, Oxford University Press, 2003.
• [TSA]: Time Series Analysis with Applications in R, Springer
• [CN]: Class Notes and Handouts
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Course Category
Mathematics and Basic Sciences %0
Social Sciences %70
Field %30

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 30
Assignment 1 % 15
Project 1 % 25
Final examination 1 % 30
Total
4
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 5 70
Assignments 1 24 24
Mid-terms 1 3 3
Practice 14 2 28
Project 1 48 48
Final examination 1 3 3
Total Work Load   Number of ECTS Credits 7 218

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Students will be asked to conduct their own data analysis using econometric techniques learned from this course.
2 They will be able to choose for the best estimation methodology in accord with the merits and demerits.
3 This course will enable them to carry out a comprehensive time series analysis of economic data.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Weighted Least Squares (WLS), Generalized Least Squares (GLS) ETM Ch 7, EM Ch 5, CN
2 Maximum Likelihood Estimation (MLE) ETM Ch 10, GME Ch6 EM Ch 5, CN
3 Principles of Testing (Wald, LR, LM) & Non Linear Regression (NLS) GME Ch6, ETM Ch 6, CN
4 Generalised Method of Moments GME Ch 5, EM Ch10
5 Binary Choice Models & Multi-response Models GME Ch7, EM Ch13
6 Univariate Time Series: Unit root /Stationarity Tests ATE Ch2, EM Ch7
7 Models for Stationary Time Series TSA Ch1,2,3,4, CN
8 Models for Non Stationary Time Series, Model Specification and Parameter Estimation TSA Ch5,6,7, EM Ch7, CN
9 Model Diagnostics, Forecasting & Seasonal Models TSA Ch8,9,10, CN
10 Multivariate Single Equation Time Series Models (DL, ARDL Models) GME Ch9, TSA Ch 11, EM Ch8, CN
11 Cointegration, Error Correction Model GME Ch9, EM Ch8, CN
12 Vector Auto Regressive Models, Cointegration in VAR GME Ch9, ATE Ch3, EM Ch8, CN
13 Granger Causality, Vector Error Correction Model ATE Ch3, GME Ch 9, EM Ch8, CN
14 Time Series Models of Heteroskedasticity TSA Ch12, GME Ch 8, ATE Ch5, CN

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8
All 5 5 4 5 5 5 5 5
C1 5 5 4 5 5 5 5 5
C2 5 5 5 5 5 5 5 5
C3 5 5 5 5 5 5 5 5

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
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