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


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Course code:
EKM2A
Course title in Czech:
Econometrics II
Course title in English:
Econometrics II
Semester:
WS 2019/2020
Mode of completion and number of credits:
Exam (5 credits)
Mode of delivery and timetabled classes:
full-time, 2/2 (hours of lectures per week / hours of seminars per week)
Level of course: master continuing
Course type:
required
Type of delivery: usual
Mode of delivery for our mobility students abroad: -- item not defined --
Language of instruction:
English
Course supervisor:
doc. Ing. Václav Adamec, Ph.D.
Course supervising department:
Faculty: Faculty of Business and Economics
Teachers:
doc. Ing. Václav Adamec, Ph.D. (examiner, instructor, lecturer, supervisor)
Prerequisites:
 
Timetable in this semester:
Day
From-till
Room
Teacher
Entry
Frequency
Capacity
Thursday
15.00-16.50Q23LectureEvery week48
Friday9.00-10.50Q37V. Adamec
Seminar
Every week
24
Friday
11.00-12.50
Q37V. AdamecSeminarEvery week
24
 
Aim of the course and learning outcomes:
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 content:
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 activities and teaching methods:
Type of teaching method
Daily attendance
lecture
28 h
practice28 h
consultation
0 h
preparation for exam
30 h
preparation for regular assessment
5 h
preparation for regular testing19 h
writing of seminar paper30 h
Total
140 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.
 
Assessment criteria ratio:
Requirement typeDaily attendance
Total0 %
 
Recomended reading and other learning resources:
Basic:
GUJARATI, D N. -- PORTER, D C. Basic econometrics. 5th ed. Boston: McGraw-Hill Irwin, 922 p. ISBN 978-007-127625-2.
GREENE, W H. Econometric analysis. 7th ed. Boston [u.a.]: Pearson, 2012. 1238 p. ISBN 978-0-273-75356-8.
WOOLDRIDGE, J M. Introductory econometrics: a modern approach. 4th ed. Mason, Ohio: South-Western, 2008. 865 p. ISBN 978-0-324-66054-8.

Recommended:
KMENTA, J. Elements of econometrics. 2nd ed. Ann Arbor: University of Michigan Press, 2011. 786 p. ISBN 0-472-10886-7.
STOCK, J H. -- WATSON, M W. Introduction to econometrics. 3rd ed. Boston, Mass. [u.a.]: Pearson, 2012. 827 p. ISBN 978-1-4082-6433-1.
HILL, C R. -- GRIFFITHS, W E. -- JUDGE, G G. Undergraduate econometrics. 2nd ed. New York: John Wiley & Sons, 2001. 402 p. ISBN 0-471-33184-8.
Introductory statistics for business and economics. 4th ed. New York: Wiley, 815 p. ISBN 0-471-51732-1.

Course listed in study plans for this semester:
Field of study C-EMAJ-MEEN Business Economics and Management, full-time form, initial period SS 2018/2019
Field of study C-EMAJ-MEEN Business Economics and Management, part-time form, initial period WS 2019/2020
Field of study C-EMAJ-MEEN Business Economics and Management, full-time form, initial period WS 2019/2020
 
Course listed in previous semesters:
WS 2020/2021, WS 2018/2019, WS 2017/2018, WS 2016/2017, WS 2015/2016, WS 2014/2015 (and older)
Teaching place:
Brno


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

Type of output: