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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS ERA 3402 ECONOMETRICS ELECTIVE 3 0 0 5

Offered By

Econometrics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

Offered to

Econometrics (Evening) Econometrics

Course Objective

The main objective of the course is to provide How to get more knowledge and skill in the field of quantitative or theorical econometrics modelling

Learning Outcomes of the Course Unit

1   To be able to identify theory of economics by using advanced econometrics techniques in modelling of econometrics 2   To be able to set up right functional form of model and related and unrelated variables in selection of identification of the ideal econometrics model 3   To be able to consitute dummy variables to measure effects of qualitative variables in regression analysis, explain usage of constant and slope with dummy variables 4   To be able to study short and long term economics affects via finite distributed lag models and infinite distributed lag models 5   To be able to identify how to be expectations with dynamic econometrics model 6   To be able to estimate system of two or more simultaneous equations 7   To be able to identify characteristics of time series variables to show effects of short and long term via dynamic econometrics models

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

HAZIRLIK - FOREIGN LANGUAGE PREPARATION CLASS

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Model specification, specification error, distinction of incorrect specification error, types of specification error 2 Methods of identifying specification error, measuring error in variables 3 Models with dummy variables, models with one dummy variable(analysis of variance model), models with dummy variables and the other quantitative variables(analysis of co-variance model) 4 Dummy variables with more than two categories and how to affect these variables eachother, test of season effect, partial linear regression 5 Models with dependent dummy variables: probability of linear model(dogrusal ihtimalli model), logistic model 6 Models with dependent dummy variables: Probit model, maximum likelihood method 7 Distributed lag models, concepts of lag and different techniques about distributed lag models, Almon Polinomial Model 8 Mid-term 9 Mid-term 10 Different techniques (continue): Koyck Model, adaptive expectation model, partial improving model 11 Estimation Methods of Autoregressive Models: estimation of autoregressive models by using least square method, estimation of autoregressive models by using proxy variable method, identify autocorrelation in autoregressive models 12 Models with simultaneous equation, identify of models with simultaneous equation, expression of mathematics, systems of sequence equation 13 Distriction of structural and reduced models, deviation of simultaneous equation, estimations of simultaneous equation 14 Econometrics of time series, stationarity, unit roots, causality in economics: Granger Test

Recomended or Required Reading

Fundamental Reference: 1. Ekonometri II, Şahin Akkaya, M .Vedat Pazarlıoğlu Supplementary Reference: 1. Ekonometri Temel Kavramları, Selahattin Güriş, Ebru Çağlayan, DER Yayınları 2. Temel Ekonometri, Damodar N. Gujarati

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

English

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 2 24 Preparation for midterm exam 1 25 25 Preparation for final exam 1 35 35 Midterm 1 1 1 Final 1 1 1 TOTAL WORKLOAD (hours) 122

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 LO.7 1