| Semester | Course Unit Code | Course Unit Title | T+P+L | Credit | Number of ECTS Credits |
| 6 | ECON302 | ECONOMETRICS II | 3+0+0 | 3 | 5 |
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Language of Instruction
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English
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Level of Course Unit
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Bachelor's Degree
|
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Department / Program
|
ECONOMICS
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Type of Program
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Formal Education
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Type of Course Unit
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Compulsory
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Course Delivery Method
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Face To Face
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Objectives of the Course
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Introducing time series data and simple and multiple linear regression techniques for its analysis.
Showing how to identify the violation of linear model classical
assumptions in time series setting.
Introducing diagnostic testing methods for better econometric model specifications.
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Course Content
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Statistics and econometrics knowledge that is necessary to understand and conduct econometric analysis is developed further as a continuation of ECON 301 – Econometrics I. Examinations of basic and common time series models and their widely used estimation methods are included in course content. Classical assumptions of consistent and efficient parameter estimations of linear models are assessed. In addition, relaxation of these assumptions and some example methods for more convenient inference are discussed.
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Course Methods and Techniques
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Course applications are done by using STATA software
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Prerequisites and co-requisities
|
( ECON301 )
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Course Coordinator
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None
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Name of Lecturers
|
Asist Prof.Dr. Eda Ustaoglu eda.ustaoglu@agu.edu.tr
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Assistants
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Research Assist. Gulcan Doganay gulcan.doganay@agu.edu.tr
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Work Placement(s)
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No
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Recommended or Required Reading
Course Category
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Mathematics and Basic Sciences
|
%30
|
|
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Social Sciences
|
%70
|
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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
|
|
Yarıyıl İçi Çalışmalarının Başarı Notunun Katkısı
|
1
|
%
20
|
|
Quiz/Küçük Sınav
|
1
|
%
25
|
|
Ödev
|
1
|
%
20
|
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Final examination
|
1
|
%
35
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Total
|
4
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%
100
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ECTS Allocated Based on Student Workload
|
Activities
|
Total Work Load
|
|
Ev Ödevi
|
5
|
5
|
25
|
|
Sınıf İçi Aktivitesi
|
14
|
3
|
42
|
|
Okuma
|
3
|
14
|
42
|
|
Araştırma
|
3
|
14
|
42
|
|
Final Sınavı
|
1
|
2
|
2
|
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Total Work Load
| |
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Number of ECTS Credits 5
153
|
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
| No | Learning Outcomes |
|
1
| Apply diagnostic testing for econometric model specification in time series models. |
|
2
| Determine the measurement errors in model’s functional forms in time series estimations. |
|
3
| Detect the presence of omitted and unnecessary variables in time series models. |
|
4
| Choose appropriate model to remedy autocorrelated error-term problem. |
Weekly Detailed Course Contents
| Week | Topics | Study Materials | Materials |
| 1 |
Introduction and Basic Concepts
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| 2 |
Characteristics of Time Series
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| 3 |
Classical Linear Model Assumptions in Time Series Context
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| 4 |
Autocorrelation: Definition and Consequences
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| 5 |
Heteroskedasticity in Time Series
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| 6 |
Serial correlation and heteroscedasticity in time series regressions
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| 7 |
LWF
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| 8 |
Review and Midterm Exam
|
|
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| 9 |
Stationarity and Unit Root Testing
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| 10 |
Pooling cross sections across time: Simple panel data methods
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| 11 |
Variable Transformations and Differencing
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| 12 |
Panel Data-I
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| 13 |
Panel Data-II
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| 14 |
Panel Data-III
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Contribution of Learning Outcomes to Programme Outcomes
Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant
https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=70498&lang=en