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Topics in Advanced Econometrics FS & HS 2020

This course covers the following three subjects: cointegration analysis, state space models, and bootstrapping procedures. These topics figure prominently in modern econometrics, both theoretical and applied. In particular, cointegration analysis is instrumental for an understanding of nonstationary time series, and this course will look closely at the ins and outs of the popular Johansen procedure. The state space representation, in turn, is a convenient way of specifying a host of different econometric models, from unobserved components models to nonlinear regressions to Bayesian models. This course will examine the workings of the Kalman filter and some of its extensions used to estimate state space models. Finally, over the past two decades the bootstrap has become ever more popular for conducting statistical inference that is more accurate in small samples than the usual asymptotic approximations and for replacing intractable analytical analyses by numerical simualtion. This course provides a theoretical and an applied introduction to this literature.
Kurs ID
CORE803
Art des Kurses
Promotion LV
Wochenstunden
2,0
ECTS
3
Semester
FS 2020
Vortragssprache
Englisch
Vortragende/r
Prof. Dr. Michael Massmann
Bitte beachten Sie, dass AustauschstudentInnen im BSc-Programm der WHU eine höhere Anzahl an Credits erwerben als hier aufgeführt. Für weitere Informationen wenden Sie sich bitte direkt an das [International Relations Office].
This course covers the following three subjects:
• cointegration analysis,
• state space models, and
• bootstrapping procedures.
These topics figure prominently in modern econometrics, both theoretical and applied. In
particular, cointegration analysis is instrumental for an understanding of nonstationary
time series, and this course will look closely at the ins and outs of the popular Johansen
procedure. The state space representation, in turn, is a convenient way of specifying a
host of different econometric models, from unobserved components models to nonlinear
regressions to Bayesian models. This course will examine the workings of the Kalman filter
and some of its extensions used to estimate state space models. Finally, over the past two
decades the bootstrap has become ever more popular for conducting statistical inference
that is more accurate in small samples than the usual asymptotic approximations and for
replacing intractable analytical analyses by numerical simualtion. This course provides a
theoretical and an applied introduction to this literature.
The presentation of these methods in the lectures will be interlaced with computer
simulations and empirical illustrations as well as with exercises.
Date Time
Monday, 22.06.2020 10:15 - 15:15
Tuesday, 23.06.2020 10:15 - 15:15
Wednesday, 24.06.2020 10:15 - 15:15
Thursday, 25.06.2020 10:15 - 15:15
A reading list will be disseminated at the beginning of the course.
By the end of the course participants will have gained a sound understanding of the three
topics covered in this course, both from a methodological and an applied perspective.
They will be able to follow the state-of-the-art literature on these topics and apply them
in empirical econometric analyses of their own.
Each participant is requested to work on a given topic over the summer months and
present his/her results in a 20-minute talk at the September session. The homework
assignment as well as the presentation will be carried out individually. A 3- to 5-page
report must be submitted one week prior to the presentation and will be disseminated
amongst participants . The topic can be theoretical, empirical or simulation-based. It
is important that both report and presentation reflect the fact that this is a course in
econometrics. That is to say, the emphasis of the treatment should lie on
• a clear exposition of the econometric model,
• a detailled description of the econometric methods, and
• a critical econometric discussion of the results.Standard econometric notation should be used throughout. Computer code and empirical
data should be made available, e.g. by attaching appropriate files to the pdf document of
the report
Familiarity with the contents in such textbook as Stock & Watson (2015) is of paramount importance. In particular, participants are urged to review the four chapters on multivariate regression and the three chapters on time series analysis. Similarly, working knowledge of basic probability theory is assumed, see for instance the two review chapters in Stock & Watson (2015). The companion website of the book contains multiple choice tests and quizzes, convenient for self-study.
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