Dokuz Eylül University Information Package / Courses Catalog

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type Of Course	D	U	L	ECTS
DLY 5029	TRANSPORT ECONOMETRICS I	ELECTIVE	3	0	0	6

Offered By

Logistics Management

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR HAMDI EMEÇ

Offered to

Logistics Management

Course Objective

The objective is to provide students with a generic background in the application of various statistical and econometric analysis techniques and to provide new ideas for analyzing data in research. The course will present a number of model-estimation methods that are used in transportation data analysis and other subject areas that deal with data analysis.

Learning Outcomes of the Course Unit

1	Understanding of fundamentals of transport economics
2	Estimation of regression models
3	Utilizing tests and model selection criteria of econometric models
4	Detecting and removing of issues such as multicollinearity, heteroscedasticity and autocorrelation
5	Understanding of main and alternative model estimation techniques
6	Utilization of econometric model development softwares

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week	Subject	Description
1	Course introduction
2	Scarcity and Choice, Supply and Demand
3	Market Demand and Elasticity
4	Input Resolutions, Cost - Benefit Analysis
5	Regression analysis with time series data
6	Output - Price Decisions
7	Midterm exam
8	Simple and multiple regression model
9	Alternative tests regarding single equation econometric models, model selection criteria
10	Multicollinearity, estimations of Least Square Regression in case Multicollinearity, detecting and removing Multicollinearity, assumption of normality of errors
11	Heteroscedasticity, estimations of Least Square Regression in case of Heteroscedasticity and consequences after Heteroscedasticity, detecting and removing Heteroscedasticity
12	Autocorrelation, estimations of Least Square Regression in case Autocorrelation and consequences after Autocorrelation detecting and removing Autocorrelation
13	Applications and term paper presentations
14	Applications and term paper presentations

Recomended or Required Reading

Washington, S., Karlaftis, M. and Mannering, F. Statistical and Economic Methods For Transportation Data Analysis 1st Ed, CRC Press, London 2003
Washington, S., Karlaftis, M. and Mannering, F. Statistical and Economic Methods For Transportation Data Analysis 2nd Ed, CRC Press, London 2011
Karakitsos, E. and Varnavides, L. Maritime Economics A Macroeconomic Approach 1st Ed. Palgrave Macmillan, UK 2014
Goodwin, E. and Kemp, J. Marine Statistics Theory and Practice 1st Ed, London 1979
Barda, Süleyman. Ulaştırma Ekonomisi Dersleri. Menteş Kitabevi : Istanbul 1982
Berg-Andreassen, Jan A. (1966) Some Properties of International Maritime Statistics. Maritime Policy and Management, 23 (4)
Buxton, I.L. Engineering Economics and Ship Design. The British Shipping Research Association : Wallsend 1971
Evans, J.J. ve Marlow, Peter. Quantative Methods in Maritime Economics. Fairplay Publications : London 1990

Planned Learning Activities and Teaching Methods

A number of applications and methods will be presented in the class that have broad applications to a variety of data-analysis in transportation engineering and beyond. The material covered goes well beyond the techniques typically covered in statistics courses. While, the course will emphasize model estimation and application, the underlying theory and limitations will be discussed to ensure that the methods are properly applied and understood. The students will be able to apply the concepts learned in the class using real world data and will also learn econometric model development in softwares such as R, Matlab, Stata, Biogeme, Eviews, Limdep and SPSS.

Assessment Methods

SORTING NUMBER	SHORT CODE	LONG CODE	FORMULA
1	MTE	MIDTERM EXAM
2	STT	TERM WORK (SEMESTER)
3	FCGR	FINAL COURSE GRADE
4	FCGR	FINAL COURSE GRADE (RESIT)	MTE * 0.20 + STT * 0.20 + FN* 0.60
5	RST	RESIT
6	FCGR	FINAL COURSE GRADE (RESIT)	MTE * 0.20 + STT * 0.20 + RST* 0.60

Further Notes About Assessment Methods

None

Assessment Criteria

Knowledge, skills and competencies in research, examination, critics, application and presentation of econometrics using transportation industry data.

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	2	26
Tutorials	13	2	26
Preparations before/after weekly lectures	13	2	26
Preparation for midterm exam	1	15	15
Preparation for final exam	1	25	25
Preparing presentations	1	30	30
Final	1	1	1
Midterm	1	1	1
TOTAL WORKLOAD (hours)			150

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	PO.11	PO.12	PO.13
LO.1	5	5	5	5	5	5	4	4	4	5	5	5	4
LO.2	4	4	4	4	4	4	5	4	4	4	4	4	4
LO.3	4	4	4	5	4	4	4	5	4	5	4	4	5
LO.4	4	4	4	4	4	5	5	4	5	4	4	5	4
LO.5	4	4	4	4	4	4	4	4	4	5	4	4	4
LO.6	4	4	4	4	4	4	4	5	4	4	5	4	5

COURSE UNIT TITLE

DEGREE PROGRAMMES

Description of Individual Course Units

Offered By

Level of Course Unit

Course Coordinator

Offered to

Course Objective

Learning Outcomes of the Course Unit

Mode of Delivery

Prerequisites and Co-requisites

Recomended Optional Programme Components

Course Contents

Recomended or Required Reading

Planned Learning Activities and Teaching Methods

Assessment Methods

Further Notes About Assessment Methods

Assessment Criteria

Language of Instruction

Course Policies and Rules

Contact Details for the Lecturer(s)

Office Hours

Work Placement(s)

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes

CONTACT INFORMATION