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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EMT 3020 COMPUTER-AIDED APPLICATIONS OF ECONOMETRICS ELECTIVE 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 Econometrics (Evening)

Course Objective

The main objective of the course is to teach the practice of econometric techniques in modeling equation by using computer software after having econometric background

Learning Outcomes of the Course Unit

1   To be able to estimate linear and nonlinear regression models with the help of using computer software. 2   To be able to interpret regression models. 3   To be able to check and test the assumptions of classical linear regression model. 4   To be able to estimate and interpret model for time series data and cross-section data.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Before running regression model, examining and classifying datasets. 2 Before running regression model, inputting dataset in computer software. 3 Estimating and interpreting regression model for fundamental macroeconomic variables by using computer software. 4 Transforming datasets in nonlinear regression model by using computer software, obtain models, and interpret models. 5 Checking and testing the assumptions of a regression model with single equation by using computer software. 6 Preventing violations of the assumptions of a regression model with single equation by using computer software. 7 Preventing violations of the assumptions of a regression model with single equation by using computer software(continue) 8 Midterm 9 Midterm 10 Checking the assumptions of autoregressive models by using computer software. 11 Addressing the models related to dependent and independent dummy variables. 12 Analyzing time series data by using computer software. 13 Analyzing time series data by using computer software(continue) 14 Introducing Multivariate models, constructing multivariate models, and analyzing multivariate models

Recomended or Required Reading

E-Views Uygulamalı Temel Ekonometri- Makro Ekonomik Verilerle, Doç. Dr. Dina Çakmur YILDIRTAN,2011

Planned Learning Activities and Teaching Methods

1- Lecture Method, 2- Demonstration Method with Applications

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 12 3 36 Preparations before/after weekly lectures 12 3 36 Preparation for midterm exam 1 25 25 Preparation for final exam 1 25 25 Midterm 1 1 1 Final 1 1 1 TOTAL WORKLOAD (hours) 124

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 1 LO.2 1 1 LO.3 1 1 LO.4 1 1