Course syllabus EKM2A - Econometrics II (FBE - WS 2019/2020)


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Course code: EKM2A
Course title in language of instruction: Econometrics 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)
Language of instruction: English
Level of course: master continuing
Semester: WS 2019/2020
Name of lecturer: doc. Ing. Václav Adamec, Ph.D. (examiner, instructor, lecturer, supervisor)
Prerequisites: Final Bachelor Exam
 
Aims of the course:
Students shall receive knowledge and practical experience with construction of advanced linear and nonlinear econometric models, especially multivariate, OLS and GLS estimation, classical assumptions and problems associated with violations of these assumptions. Students will learn to perform comprehensive and advanced econometric analysis on real cross-sectional and time-series data, including Box-Jenkins methods. Students can identify and handle departures from standard conditions. Students develop skills to apply advanced econometric methods with statistical software.
 
Course contents:
1.Multiple linear regression model (allowance 6/6)
 
a.Introduction to Econometry
b.Ordinary Least Squares and Generalized Least Squares
c.Applications of regression analysis, ways to construct econometric model
d.Multiple classical regression model
e.Hypothesis testing, confidence intervals, forecasting
f.Advanced models of ANCOVA

2.Violations of classical assumptions (allowance 8/6)
 
a.Multiple model construction, methods and criteria, model overfitting, underfitting
b.Serial correlation, causes, consequences, methods of detection, remedy
c.Heteroskedasticity, causes, consequences, methods of detection, remedy
d.Multicollinearity, causes, consequences, methods of detection, remedy

3.Advanced time-series modelling (allowance 6/8)
 
a.Stochastic time series modelling
b.Box-Jenkins approach, stationary and integrated processes
c.Models of structural change, QLR test, Chow test
d.Regression models of time series

4.Analysis of multivariate time series (allowance 2/3)
 
a.Vector autoregressive models (VAR), specification, lag determination
b.Estimation, verification, Granger causality tests
c.Vector moving averages models (VMA)
d.Vector autoregressive moving averages models (VARMA)

5.Nonlinear regression (allowance 2/3)
 
a.Motivation, applications in Econometry
b.Nonlinear Least Squares
c.Estimation techniques (Gauss-Newton, Marquardt), applications
d.Analysis of binary response, binary logit and probit models

 
Learning outcomes and competences:
Generic competences:
 
-Ability to analyse and synthesize
-Ability to comment on performance of others and to self-reflect
-Ability to solve problems
-Communication in second language
-Science and research skills
-Skilled at utilizing and processing information

Specific competences:
 
-Students are able to apply the theory of multiple linear regression model to real data.
-Students are able to apply the VAR model and interpret the results.
-Students are able to create models of univariate time series using Box-Jenkins methodology.
-Students are able to detect and solve problems arising in the construction of multiple linear regression model.
-Students are able to work with non-linear models.
-Students are familiar with and 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
     consultation0 h
Self-study
     preparation for exam30 h
     preparation for regular assessment5 h
     preparation for regular testing19 h
     writing of seminar paper30 h
Total140 h
 
Assessment methods:
A credit is granted on the basis of group project (>= 50% score), midterm exam (score at least 50%) and active participation in labs (at most 2 skipped labs). Active lab participation is further assessed by 1 point per lab session. Credit is required for admission to the final exam. Passing final exam requires at least 50% score. Course grade is made on the basis of the final exam, midterm test, project and active participation: A [90 – 100]; B [80 – 90); C [71 – 80); D [62 – 71); E [53 – 62); F [0 – 53). The examiner may readjust the grade by 1 step in both directions. The course cannot be taken during overseas internship.
 
Recommended reading:
TypeAuthorTitlePublished inPublisherYearISBN
RQGUJARATI, D N. -- PORTER, D C.Basic econometricsBostonMcGraw-Hill Irwin978-007-127625-2
RQGREENE, W H.Econometric analysisBoston [u.a.]Pearson2012978-0-273-75356-8
RQWOOLDRIDGE, J M.Introductory econometrics: a modern approachMason, OhioSouth-Western2008978-0-324-66054-8
REKMENTA, J.Elements of econometricsAnn ArborUniversity of Michigan Press20110-472-10886-7
RESTOCK, J H. -- WATSON, M W.Introduction to econometricsBoston, Mass. [u.a.]Pearson2012978-1-4082-6433-1
REHILL, C R. -- GRIFFITHS, W E. -- JUDGE, G G.Undergraduate econometricsNew YorkJohn Wiley & Sons20010-471-33184-8
REIntroductory statistics for business and economicsNew YorkWiley0-471-51732-1

RQrequired
RErecommended


Last modification made by Ing. Jiří Gruber on 09/05/2019.

Type of output: