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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
IKT 805ECONOMETRIC APPLICATION3 + 02nd Semester7,5

COURSE DESCRIPTION
Course Level Master's Degree
Course Type Elective
Course Objective To describe the core econometrik techniques using E-views and stata programs. To teach how these techniques are used for the test of economic theories and how to interpret the findings
Course Content Linear Regression Model with one regressor, Linear Regression model with multiple regressors, hypothesis testing, detection and correction of the estimation problems, instrumental variable and 2SLS estimator, time series models and estimation techniques
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Makes the data analyzes and regulation required for econometric modelling
2Estimates the one regressor and multiple regressor regression model and interprets the findings
3Tests the hypotheses
4Estimates the time series models and interprets the findings

COURSE'S CONTRIBUTION TO PROGRAM
Data not found.

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)13565
Hours for off-the-classroom study (Pre-study, practice)13339
Assignments21326
Final examination13939
Midterm12626
Total Work Load

ECTS Credit of the Course






195

7,5
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2019-2020 Fall1MEHMET İVRENDİ


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Course Details
Course Code Course Title L+P Hour Course Code Language Of Instruction Course Semester
IKT 805 ECONOMETRIC APPLICATION 3 + 0 1 Turkish 2019-2020 Fall
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Prof. Dr. MEHMET İVRENDİ mivrendi@pau.edu.tr İİBF A0202 %
Goals To describe the core econometrik techniques using E-views and stata programs. To teach how these techniques are used for the test of economic theories and how to interpret the findings
Content Linear Regression Model with one regressor, Linear Regression model with multiple regressors, hypothesis testing, detection and correction of the estimation problems, instrumental variable and 2SLS estimator, time series models and estimation techniques
Topics
Materials
Materials are not specified.
Resources
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam50Final Exam
Midterm Exam50Midterm Exam
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes