1. FIRST CYCLE - BACHELOR'S DEGREE
  2. SCHOOL OF APPLIED SCIENCES 
  3. CAPITAL MARKET DEPARTMENT
  4. 424 CAPITAL MARKET (Evening Classes)
Course Information Course Learning Outcomes Course's Contribution To Program ECTS Workload Course Details
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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
SER 125BASIC ECONOMETRICS4 + 05th Semester6

COURSE DESCRIPTION
Course Level Bachelor's Degree
Course Type Compulsory
Course Objective To help students develop and evaluate classic linear, semi logarithmic and logarithmic regression models
Course Content The main aim of this lesson is to help students develop and evaluate classic linear, semi logarithmic and logarithmic regression models. Basic concepts about statistics and econometrics, basic features of random sampling, Assumptions of linear regression model, features of estimators, forecasting the parameters of least squares method, deviations from the average, estimating normal equations in terms of original observation, Interpreting the parameters of simple linear regression Eviews Application, Simple correlation and examples, rank correlation and examples, Determination coefficient, testing the significance of parameter estimates and examples, Hypothesis Tests Practical t test, t test and examples, Variable transformation in models, Testing the significance of correlation coefficient, Elasticity and confidence levels for parameters and examples, Analysis of multivariate linear regression model; ssumptions of multivariate linear regression model, Deviations from the average in multivariate models and finding normal equaitons in terms of original observations, commenting on the parameters of multivariate regression model, Estimation of non-linear regression model, estimating Cobb-Douglas type production function, Estimating and commeting on the patterns of various econometric functions Eviews application, Elasticities and slopes about various econometric models and examples, Scale return and changing return will be discussed as the main topics during the semester
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Compares the economic and financial data and the generated functions
2Concludes the economic and financial data to draw about the significance of the function
3Interprets the regression coefficients of the form-based function

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10
LO 001  3 5 5   
LO 002  3 5 5   
LO 003  3 5 5   
Sub Total  9 15 15   
Contribution0030505000

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14456
Hours for off-the-classroom study (Pre-study, practice)14684
Mid-terms166
Final examination11010
Total Work Load

ECTS Credit of the Course





156

6
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2020-2021 Fall1EDA YALÇIN KAYACAN


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Course Details
Course Code Course Title L+P Hour Course Code Language Of Instruction Course Semester
SER 125 BASIC ECONOMETRICS 4 + 0 1 Turkish 2020-2021 Fall
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Assoc. Prof. Dr. EDA YALÇIN KAYACAN eyalcin@pau.edu.tr UBYO A0016 %60
Goals To help students develop and evaluate classic linear, semi logarithmic and logarithmic regression models
Content The main aim of this lesson is to help students develop and evaluate classic linear, semi logarithmic and logarithmic regression models. Basic concepts about statistics and econometrics, basic features of random sampling, Assumptions of linear regression model, features of estimators, forecasting the parameters of least squares method, deviations from the average, estimating normal equations in terms of original observation, Interpreting the parameters of simple linear regression Eviews Application, Simple correlation and examples, rank correlation and examples, Determination coefficient, testing the significance of parameter estimates and examples, Hypothesis Tests Practical t test, t test and examples, Variable transformation in models, Testing the significance of correlation coefficient, Elasticity and confidence levels for parameters and examples, Analysis of multivariate linear regression model; ssumptions of multivariate linear regression model, Deviations from the average in multivariate models and finding normal equaitons in terms of original observations, commenting on the parameters of multivariate regression model, Estimation of non-linear regression model, estimating Cobb-Douglas type production function, Estimating and commeting on the patterns of various econometric functions Eviews application, Elasticities and slopes about various econometric models and examples, Scale return and changing return will be discussed as the main topics during the semester
Topics
WeeksTopics
1 Introduction to Econometrics
2 Simple Regression Modeli
3 OLS estimator and assumptions
4 Properties of OLS estimator: Gauss-Markow Theorem
5 Maximum Likelihood Method
6 Hypothesis testing
7 Alternative functional forms of regression models
8 Midterm exam
9 Introduction to Multiple Regression Models
10 Estimation of Mutliple Regression Model
11 Statistical inference
12 Structural Stability Tests
13 Matrix approach to regression model-I
14 Matrix approach to regression model-II
Materials
Materials are not specified.
Resources
ResourcesResources Language
Gujarati, D. ve Porter, D. "Temel Ekonometri",5.baskı,İngilizde'den ÇeviriTürkçe
Gujarati, D. ve Porter, D. "Temel Ekonometri",5.baskı,İngilizde'den ÇeviriTürkçe
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam60Final Exam
Midterm Exam40Midterm Exam
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes