INMNPEKM Econometrical Methods

School of Business Administration in Karvina
Winter 2016
Extent and Intensity
2/1/0. 4 credit(s). Type of Completion: zk (examination).
Teacher(s)
Mgr. Radmila Krkošková, Ph.D. (lecturer)
Mgr. Radmila Krkošková, Ph.D. (seminar tutor)
Guaranteed by
Mgr. Radmila Krkošková, Ph.D.
Department of Informatics and Mathematics – School of Business Administration in Karvina
Contact Person: Mgr. Radmila Krkošková, Ph.D.
Prerequisites (in Czech)
K absolvování předmětu nejsou vyžadovány žádné podmínky a předmět může být zapsán nezávisle na jiných předmětech.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The objective is to provide a deeper insight into econometric modelling of economic situations, provide theoretical tools used by selected econometric methods, and show their application in economic disciplines involving microeconomics, macroeconomics and finance. Software packages used to solve such problems are also presented.
Syllabus
  • 1. Econometrics as a subject, specific nature of variables in econometrics
    2. Multivariate regression analysis
    3. Assumptions in regression analysis
    4. Heteroscedasticity
    5. Autocorrelation
    6. Multicollinearity
    7. Generalized least squares method
    8. Maximum likelihood method
    9. Sets of regression equations, two-staged least squares
    10. Dynamic regression model
    11. Logistic regression model
    12. Combined structure in a model
    13. Eview and Excel software
    1. Econometrics as a subject, specific nature of variables in econometrics
    Definition of econometrics, area of study, econometric tools, classification of variables, differences as compared to general mathematics.
    2. Multivariate regression analysis
    Matrix approach to regression, testing coefficients in a model, testing the model, identification of influential points.
    3. Assumptions in regression analysis
    Essential assumptions in regression and two ways of expressing them, reasoning behind the assumptions.
    4. Heteroscedasticity
    Definition, detection of heteroscedasticity with statistical tests, graphical detection, effect of heteroscedasticity on the quality of regression, removal of heteroscedasticity.
    5. Autocorrelation
    Definition, detection of autcorrelation with statistical tests, graphical identification, effect of autocorrelation on the quality of regression, removal of autocorrelation.
    6. Multicollinearity
    Definition, detection of multicollinearity with statistical tests, effect of multicollinearity on the quality of regression, overcoming multicollinearity.
    7. Generalized least squares method (GLS)
    Derivation of GLS relations, V matrix for the case of heteroscedasticity or autocorrelation, the idea and importance of GLS, comparison of GLS and ordinary least squares.
    8. Maximum likelihood method (MLE)
    The idea of MLE, comparison of MLE and least squares, limitations in using the least squares method, advantages of MLE.
    9. Sets of regression equations, two-stage least squares
    Testing the correctness of a set of regression equations, the idea behind the two-stage least squares method
    10. Dynamic regression models
    Identification and classification of dynamic models, Fisher's solution
    11. Logistic regression model
    Generalization of regression for different types of data, models for discrete output variables, binary models, use of MLE estimation.
    12. Combined structure in a model
    Decision on using one regression function or a combination of more regression functions and a way of finding the functions.
    13. Eview and Excel software
    Calculation of the lectured subject matter, using Excel and EViews.
Literature
    required literature
  • HUŠEK, R. Ekonometrická analýza. Praha : Oeconomica, 2007. ISBN 978-80-245-1300-3. info
    recommended literature
  • KRKOŠKOVÁ, Š., RÁČKOVÁ, A., ZOUHAR, J. Základy ekonometrie v příkladech. Praha : Oeconomica, 2010. ISBN 978-80-245-1708-7. info
  • HUŠEK, R. Aplikovaná ekonometrie : teorie a praxe. Praha : Oeconomica, 2009. ISBN 978-80-245-1623-3. info
  • RAMÍK, J. Statistika pro navazující magisterské studium. Karviná: OPF SU, 2007. info
  • MEZNÍK, I. Ekonometrie pro magisterské studijní programy. Brno : Akademické nakladatelství CERM, 2005. ISBN 80-214-3039-7. info
  • DAVIDSON R., MACKINNON, J. G. Econometric Theory and Methods. New York: Oxford University Press, 2004. info
  • GURAJATI, D. N. Basic Econometrics, 4. Ed. Mc Graw-Hill, Singapore, 2003. ISBN 0-07-233542-4. info
Teaching methods
Skills demonstration
Seminar classes
Assessment methods
Written exam
Written test
Language of instruction
Czech
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
Teacher's information
Written examination, 70% attendance in seminars.
ActivityDifficulty [h]
Ostatní studijní zátěž36
Přednáška26
Seminář13
Zkouška40
Summary115
The course is also listed under the following terms Winter 2014, Winter 2015, Winter 2017, Winter 2018, Winter 2019, Winter 2020.
  • Enrolment Statistics (Winter 2016, recent)
  • Permalink: https://is.slu.cz/course/opf/winter2016/INMNPEKM