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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EKO 5010 REGRESSION ANALYSIS ELECTIVE 3 0 0 4

Offered By

Econometrics

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR ALI KEMAL ŞEHIRLIOĞLU

Offered to

Econometrics

Course Objective

The aim of this course is to explain mathematical relationships between the dependent and independent variables and to teach regressions parsed examples and computer applications.

Learning Outcomes of the Course Unit

1   To be able to interpret regression outcomes 2   To be able to create a proper regression equation 3   To be able to explain the coefficients using analysis of variance 4   To be able to use computer programs to interpreting outputs 5   To be able to apply multivariate regression analysis methods

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Maximum Likelihood Method 2 Binary Choice Models 3 Tobit Models, Censored and Discrete Regression Models, The Problem of Censoring in Tobit models, The Problem of Censoring as Theoretical 4 Tobit Model at the censored results, Connection Between Tobit and Probit, Comparison of Tobit Model OLS with The An Example, Selection Bias, The Applications Related Tobit Models 5 Modeling by Using Time-Series Data 6 Seemingly Unrelated Regression Models 7 The Applications Related with Seemingly Unrelated Regression models 8 Midterm 9 The Bayesian Approach to Estimation and Interpretation: Some Basic Concepts and Applications 10 The Interval Estimation in Bayesian interpretation , Hypothesis Testing, Estimation, Knowledge of Variance. 11 The Applications of Bayesian Approach to Estimation and Interpretation 12 The Bayesian Approach of Estimation the Relationships Between Economic Variables, The Forecasting and Interpretation Based on The Method of Sampling Theory, The Expression of Uncertainly Related The Parametres of Consumption Function in Situation Knowing Variance and Not Being Prior Information. 13 The Point Estimation of Probability Statements Obtained from Discrete Normal Distributions. 14 The Applications Related with Bayesian Approach to Estimate The Relationships Between Economic Variables.

Recomended or Required Reading

1-Christopher Dougherty, Introduction to Econometrics, Oxford University Pres, 2011. 2-Griffiths, W., Hill, R.C., Judge, G.G., (1993), Learning and Practicing Econometrics, John Wiley & Sons Inc 3-Dimitrios Asterious and Stephen G. Hall, Applied Econometrics A Modern Approach 4-Damodar Gujarati, Basic Econometrics 5-Pindcyk ve Rubinfield, Econometrics Models and Economic Forecasting.

Planned Learning Activities and Teaching Methods

Lecturing, Question-Answer Method, Discussion Method and Problem Solving Method, Applications

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 FCG FINAL COURSE GRADE 3 FCGR FINAL COURSE GRADE MTE * 0.40 + FCG* 0.60 4 RST RESIT 5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + RST* 0.60 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 13 3 39 Preparations before/after weekly lectures 13 2 26 Preparation for midterm exam 1 20 20 Preparation for final exam 1 20 20 Midterm 1 3 3 Final 1 3 3 TOTAL WORKLOAD (hours) 111

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 LO.1 1 1 1 LO.2 1 1 1 LO.3 1 1 1 LO.4 1 1 1 LO.5 1 1