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 2019-2020 Fall2TAHSİN AVCI


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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 2 Turkish 2019-2020 Fall
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Lecturer TAHSİN AVCI tavci@pau.edu.tr ÇMYO A0063 %80
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 General information about financial markets: recognition of investment environment ...
2 Fundamental Factors in the Value of Financial Assets: Theory of Interest, Time Value of Money
3 Valuation of Financial Assets: Stocks and Bond Valuation
4 Portfolio Theory: Risk and Return
5 Asset Pricing Models: Capital Assets Pricing Model (CAPM)
6 Asset Pricing Models: Arbitrage Pricing Model (APM)
7 Assumptions of the Classical Linear Regression Model: Test Strategies for Assumptions
8 Time Series Analysis for Single Variable: AR, MA, ARMA Processes
9 Stationary in Time Series
10 Forecasting Procedure in Time Series and Efficiency Forms in Financial Markets: Efficient Market Hypothesis (Random Walk)
11 Autoregressive Conditional Variance Models: Single Variable ARCH-GARCH Models
12 Autoregressive Conditional Variance Models: Multivariable Variable ARCH-GARCH Models
13 Examples on BIST and World Stock Indexes
14 Examples on BIST and World Stock Indexes
Materials
Materials are not specified.
Resources
ResourcesResources Language
1.Intraductory Econometrics for Finance, Chris Brooks, Cambridge Unv. Press, 3rd. Ed. Türkçe
2. Koray KAYALIDERE, Volatilite Tahmin Modelleri ve Performanslarının Ölçümü, Gazi Kitabevi, AnkaraTürkçe
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