Course syllabus EKM2 - Econometrics II (FBE - WS 2017/2018)

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Course code: EKM2
Course title in language of instruction: Ekonometrie II
Course title in Czech: Econometrics II
Course title in English: Econometrics II
Mode of completion and number of credits: Exam (5 credits)
(1 ECTS credit = 28 hours of workload)
Mode of delivery/Timetabled classes: full-time, 2/2 (hours of lectures per week / hours of seminars per week)
part-time, 16/0 (lectures per period / seminars per period)
Language of instruction: Czech
Level of course: master continuing
Semester: WS 2017/2018
Name of lecturer: doc. Ing. Václav Adamec, Ph.D. (instructor)
RNDr. Marie Forbelská, Ph.D. (instructor)
doc. Mgr. David Hampel, Ph.D. (examiner, instructor, lecturer, supervisor)
Ing. Lenka Roubalová (instructor)
Ing. Michaela Staňková (instructor)
doc. Ing. Luboš Střelec, Ph.D. (examiner, instructor, lecturer)
Prerequisites: Final Bachelor Exam and not REO
Aims of the course:
Mastery of theory of multivariate econometric models formation using OLS method, its prerequisites and issue violations, including practical use of the methodology in the field of cross-sectional data and time series. Theoretical and practical knowledge of advanced methods of time series analysis involving the Box-Jenkins methodology and the related methods.
Course contents:
1.Classical linear regression model and its assumptions (allowance 6/6)
a.Classical linear regression model with multiple explanatory variables
b.Hypothesis testing in the linear regression model
c.Assumptions of classical linear regression model

2.Violations and remedies of classical linear regression model assumptions (allowance 6/6)
a.Model specification
c.Serial correlations

3.Specific problems of economic time series analysis (allowance 3/3)
a.Nonlinearity of time series
b.Seasonality and periodicity in time series
c.Nonstationarity of time series
d.The seeming dependence in time series
e.Cointegration of time series

4.Advanced methods for the analysis of univariate time series (allowance 8/5)
a.Stationary Box-Jenkins ARMA processes
b.Nonstationary Box-Jenkins ARIMA processes
c.Seasonal Box-Jenkins SARIMA processes
d.Volatility models

5.Advanced methods for the analysis of multivariate time series (allowance 3/2)
a.Vector autoregressive model (VAR)
b.Granger causality

6.Nonlinear models (allowance 2/2)
a.Nonlinear least squares
b.Logit model

7.Presentation of selected econometric problems (allowance 0/4)
Learning outcomes and competences:
Generic competences:
-ability to analyse and synthesize
-ability to create new ideas (creativity)
-ability to solve problems
-skilled at utilizing and processing information

Specific competences:
-Student is able to apply the theory associated with a multidimensional regression model to real data.
-Student is able to apply the VAR model and interpret the results.
-Student is able to create models of one-dimensional time series according to Box-Jenkins methodology.
-Student is able to solve problems arising in the construction of multidimensional regression model.
-Student is able to work with non-linear models.
-Student knows and is able to apply advanced econometric models and methods.

Type of course unit: required
Year of study: Not applicable - the subject could be chosen at anytime during the course of the programme.
Work placement: There is no compulsory work placement in the course unit.
Recommended study modules: -
Learning activities and study load (hours of study load):
Type of teaching methodDaily attendanceCombined form
Direct teaching
     lecture28 h16 h
     practice28 h0 h
     preparation for exam36 h50 h
     preparation for regular assessment8 h4 h
     preparation for regular testing20 h20 h
     elaboration and execution of projects20 h50 h
Total140 h140 h
Assessment methods:
For assessment, the active participation in seminars, fulfillment of the ongoing test conditions and succesful advocation of semester project are required. The course is completed by a written examination covering the theoretical and practical part. Part of the final evaluation of the course is to advocate semester project, containing the compilation and analysis of multivariate econometric model including quantification and verification. The same requirements hold for combined form of study except active participation in seminars.
Recommended reading:
TypeAuthorTitlePublished inPublisherYearISBN
RQHAMPEL, D. -- BLAŠKOVÁ, V. -- STŘELEC, L.Ekonometrie 2BrnoMendelova univerzita v Brně2012978-80-7375-664-2
RQCIPRA, T.Finanční ekonometriePrahaEkopress2008978-80-86929-43-9
RQGUJARATI, D N. -- PORTER, D C.Basic econometricsBostonMcGraw-Hill Irwin978-007-127625-2
REARLT, J. -- ARLTOVÁ, M.Ekonomické časové řady: [vlastnosti, metody modelování, příklady a aplikace]PrahaGrada2007978-80-247-1319-9
REGREENE, W H.Econometric analysisBoston [u.a.]Pearson2012978-0-273-75356-8
REKMENTA, J.Elements of econometricsAnn ArborUniversity of Michigan Press20110-472-10886-7


Last modification made by Ing. Jiří Gruber on 05/22/2017.

Type of output: