Course: Econometrics and Prognostic Science in Transportation

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Course title Econometrics and Prognostic Science in Transportation
Course code KDMML/PEPDK
Organizational form of instruction Lecture
Level of course Master
Year of study not specified
Semester Summer
Number of ECTS credits 6
Language of instruction Czech
Status of course Compulsory
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Pojkarová Kateřina, Ing. Ph.D.
Course content
Substance of the econometrics, origin and development, goals of econometrics. General econometrics model. Regress analysis, smallest squares methodology. Correlation analysis, multicolinearity, man-made variables. Time series analysis, basic characteristics of the time series. Trend part and adaptive models of time series. Seasonal and random part, correlation of the time series. Simultanious equation models. Prognostics and prognosting. Transport as a matter of prognostic. Transport prognostic modelling, four steps model.

Learning activities and teaching methods
Monologic (reading, lecture, briefing), Methods of individual activities
Learning outcomes
The basic goal of this subject is to learn students seek and measure interrelationship function relations and dependencies among economic quantities and forecast their future development. The subject is focused, except of the overview of the modern econometric procedures by construction, estimating and testing econometric models, on the implementation of these models in the economic praxis and in the transport.
After passing this subject the student can arrange and verify general econometric model, use regress and correlation analysis for modelling economic relationships, decompose time series to the single parts, including trend and seasonal part modelling and testing hypothesis about he random part. He understands definitions from the prognostics and he is oriented in basic approaches to the transport prognostic.
Prerequisites
It's expected basic knowledge of macro economy, micro economy, mathematics and statistics

Assessment methods and criteria
Oral examination, Written examination, Home assignment evaluation

The student must prove during the term and also by the final exam, that he/she understands solved problems and the ability to model basic economic relationships within the taught matter. The tutor will notify the concrete requirements to the students within the first week of the term.
Recommended literature
  • Černá, Anna. Teorie řízení a rozhodování v dopravních systémech. Pardubice: Institut Jana Pernera, 2004. ISBN 80-86530-15-9.
  • Hill, T., Lewicki, P. Statistics: Methods and Applications. Tulsa, 2006. ISBN 80-86419-26-6.
  • Hindls, Richard. Metody statistické analýzy pro ekonomy. Praha: Management Press, 1997. ISBN 80-85943-44-1.
  • Hušek R. Ekonometrická analýza. Praha, 1999.
  • Hušek R., Pelikán J. Aplikovaná ekonometrie, teorie a praxe. Praha, 2003.
  • Hušek,R. Základy ekonometrické analýzy I. Modely a metody.. VŠE, 1995.
  • Orava, František. Prognostické inženýrství v dopravě. Pardubice: Univerzita Pardubice, 2000. ISBN 80-7194-245-6.
  • Pojkarová, Kateřina. Ekonometrie a prognostika v dopravě : studijní opora. Pardubice: Univerzita Pardubice, 2013. ISBN 978-80-7395-585-4.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Transport Engineering Study plan (Version): Transport Management, Marketing and Logistics (2014) Category: Transportation and communications 1 Recommended year of study:1, Recommended semester: Summer
Faculty: Faculty of Transport Engineering Study plan (Version): Transport Management, Marketing and Logistics (2016) Category: Transportation and communications 1 Recommended year of study:1, Recommended semester: Summer
Faculty: Faculty of Transport Engineering Study plan (Version): Transport Management, Marketing and Logistics (2013) Category: Transportation and communications 1 Recommended year of study:1, Recommended semester: Summer