Course syllabus PSM - Advanced Statistical Methods and Models (FBE - SS 2019/2020)


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Course code: PSM
Course title in language of instruction: Pokročilé statistické metody a modely
Course title in Czech: Advanced Statistical Methods and Models
Course title in English: Advanced Statistical Methods and Models
Mode of completion and number of credits: Exam (6 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)
Language of instruction: Czech
Level of course: master continuing
Semester: SS 2019/2020
Name of lecturer: doc. Mgr. David Hampel, Ph.D. (examiner, instructor, lecturer, supervisor)
Prerequisites: Econometrics II
 
Aims of the course:
Theoretical and practical experience in the application of advanced statistical methods. Knowledge of the principles of work with modern computer system Matlab. Correct presentation of statistical outputs. The use of multivariate statistical methods for economic data. Students should be able effectively perform superior analysis of macroeconomic and microeconomic data after the course.
 
Course contents:
1.Basic using of the computational system Matlab (allowance 6/6)
 
a.Principle of the work in the computational system Matlab
b.Algoritmization of statistical and econometrical tasks
c.Batch processing of data

2.Presentation of statistical outputs (allowance 2/2)
 
a.Methodology of statistical outputs presentation
b.Common mistakes in presentation of numerical and graphical outputs
c."Big Data" concept

3.Selected statistical methods, tests and procedures (allowance 4/4)
 
a.Pearson's and Spearman's concept of correlation
b.Testing dependency of ordinal characteristics
c.Odds ratio
d.General linear hypothesis
e.Testing equality of at least three mean values (factor ANOVA)

4.Problems of classification (allowance 2/2)
 
a.Hierarchical cluster analysis
b.Dendrogram
c.Further classification techniques

5.Principal component analysis (allowance 2/2)
 
a.Idea of the method
b.Graphical outputs of the analysis

6.Factor analysis (allowance 2/2)
 
a.Idea of the method
b.Identification of factors

7.Panel data analysis (allowance 4/4)
 
a.Introduction to the problem
b.Practical realisation of the analysis

8.VEC model (allowance 2/2)
 
a.Definition, relation to the VAR model
b.Practical application

9.Nonlinear models in economics
  
a. Specifika odhadu parametrů nelineárních modelů
b. Vybrané nelineární modely
(allowance 2/2)
 
a.Specifics of nonlinear model parameters estimation
b.Selected nonlinear models

10.Simulation of economic phenomena (allowance 2/2)
 
a.Generation of random variables
b.Simulations related to multivariate regression model
c.Creating scenarios

 
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 simulate economical events
-Student is able to use methods dedicated for multivariate data analysis.
-Student is able to work in computational systém Matlab
-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 attendance
Direct teaching
     lecture28 h
     practice28 h
Self-study
     preparation for exam37 h
     preparation for regular assessment40 h
     preparation of presentation5 h
     writing of seminar paper30 h
Total168 h
 
Assessment methods:
During the semester, students prepare a semester project that is evaluated as successful or unsuccessful. In the case of an unsuccessful semester project, it can be reworked once. In the case of a successful semester project, students are allowed to enroll for an oral exam.

The course is possible to enroll in a foreign trip.
 
Recommended reading:
TypeAuthorTitlePublished inPublisherYearISBN
RQMastering MATLABHarlowPearson978-0-273-75213-4
RQHAMPEL, D. -- JANOVÁ, J. -- VISKOTOVÁ, L.MATLABBrnoMendelova univerzita v Brně2018978-80-7509-543-5
RQHEBÁK, P. et al.Statistické myšlení a nástroje analýzy datPrahaInformatorum2013978-80-7333-105-4
RQMELOUN, M. -- MILITKÝ, J. -- HILL, M.Statistická analýza vícerozměrných dat v příkladechPrahaAcademia2012978-80-200-2071-0
REANDĚL, J.Statistické metodyPrahaMatfyzpress2007978-80-7378-003-6
REStatistical rules of thumbHobokenWiley978-0-470-14448-0
REHEBÁK, P. et al.Vícerozměrné statistické metody [3]PrahaInformatorium200580-7333-039-3
RELÜTKEPOHL, H.New introduction to multiple time series analysisBerlinNew York :20053-540-40172-5
RETUČEK, D.Modelování, simulace a optimalizace podnikových procesů v praxi ZlínČSOP2011978-80-260-0023-5
REZAPLATÍLEK, K. -- DOŇAR, B.MATLAB pro začátečníkyPrahaBEN - technická literatura200580-7300-175-6

RQrequired
RErecommended


Last modification made by Ing. Jiří Gruber on 10/25/2019.

Type of output: