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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EMT 3005 INTRODUCTION TO ECONOMETRICS I COMPULSORY 3 0 0 5

Offered By

Econometrics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR HAMDI EMEÇ

Offered to

Econometrics (Evening) Econometrics

Course Objective

The main objective of the course is How to set up econometric model and gain abilitiy of understanding accordingly Economics Theory.

Learning Outcomes of the Course Unit

1   To be able to understand the difference between time series and cross-sectional data 2   To be able to define the purposes of econometrics model 3   To be able to choose model as reasonable econometrics method 4   To be able to analysis assumptions of econometrics model 5   To be able to make inferences from econometrics models s evidences 6   To be able to decide true econometrics analysis for data bu using convenient econometrics software

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 The purpose of econometrics, the topic of econometrics and the preceding steps in econometrics research 2 Simple Linear Regression Model(Bivariate Regression Model) 3 Least Square Regression Model and its assumptions. 4 Multivariate Regression Model 5 Hypothesis Tests, Regression and Analysis of Variance 6 Topics with Bivariate Regression Models 7 The Other Tests for econometrics models with one equation, selection of models criteria 8 Applications 9 Mid-term 10 Distribtions for Normality and Normality tests, Multicollineartity, meaning of Multicollineartity, Estimations of Least Square Regression in case Multicollineartity, consequences after Multicollineartity, detected and remove Multicollineartity 11 Heteroscedasticity, meaning of Heteroscedasticity, Estimations of Least Square Regression in case Heteroscedasticity, consequences after Heteroscedasticity 12 Detected and remove Heteroscedasticity 13 Autocorrelation, meaning of Autocorrelation, Estimations of Least Square Regression in case Autocorrelation, consequences after Autocorrelation 14 Detected and remove Autocorrelation 15 Applications

Recomended or Required Reading

Fundamental Reference: Temel Ekonometri (2012), Damodar N.GUJARATI, Dawn C. PORTER (Çev. Ümit ŞENESEN-Gülay Günlük ŞENESEN), Litaratür Yayıncılık, Istanbul. Supplementary References: Ekonometri (2014), Recep TARI, Umuttepe Yayınları, Kocaeli Üniversitesi.

Planned Learning Activities and Teaching Methods

This course will be presented using manner of telling method, question and answer method, discussion method and problem solving method.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 MTEG MIDTERM GRADE MTEG * 1 3 FIN FINAL EXAM 4 FCGR FINAL COURSE GRADE MTEG * 0.40 + FIN * 0.60 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTEG * 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 14 3 42 Preparations before/after weekly lectures 14 3 42 Preparation for midterm exam 1 14 14 Preparation for final exam 1 14 14 Midterm 1 1 1 Final 1 1 1 TOTAL WORKLOAD (hours) 114

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 PO.9 PO.10 LO.1 1 LO.2 1 LO.3 1 LO.4 1 LO.5 1 LO.6 1