0757 Applied Econometrics
Kory Johnson, M.A.,Ph.D.
  • LV-Typ
  • Semesterstunden
  • Unterrichtssprache
17.09.2020 bis 20.09.2020
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Bachelor
Wochentag Datum Uhrzeit Raum
Montag 05.10.2020 16:00 - 18:00 D5.1.003
Montag 12.10.2020 16:00 - 18:00 D5.1.003
Montag 19.10.2020 16:00 - 18:00 TC.3.03
Montag 02.11.2020 16:00 - 18:00 TC.3.03
Montag 09.11.2020 16:00 - 18:00 TC.3.03
Montag 16.11.2020 16:00 - 18:00 TC.3.03
Montag 23.11.2020 16:00 - 18:00 TC.3.03
Montag 30.11.2020 16:00 - 18:00 TC.3.08
Montag 07.12.2020 16:00 - 18:00 TC.3.08
Montag 14.12.2020 16:00 - 18:00 Online-Einheit
Montag 21.12.2020 16:00 - 18:00 TC.3.03
Montag 11.01.2021 16:00 - 18:00 TC.3.03

Ablauf der LV bei eingeschränktem Campusbetrieb

In case of limited activity on campus, this course will be conducted in synchronous hybrid mode. To this end, the participants will be split into two equal groups A and B. In one week, group A will be physically present at the lecture while group B will be able to watch the course live online (the course will be streamed). The roles of A and B alternate on a weekly basis, meaning that each student can attend the lecture on campus biweekly.
Under special circumstances (travel restrictions, health issues) it is also possible for students to participate in the course purely remotely.
More detailed issues on who is in what group will be communicated after September 15 and before the start of the term via learn or email.

Inhalte der LV

We cover:

- stationary multivariate time series models such as bivariate and multivariate vector autoregressions (VARs)
- volatility models such as GARCH and SV models
- non-stationary time series including unit root tests, VARs in the levels, cointegration, VEC models, and the Johanssen test
- if time allows and according to students' interests: panel data analysis such as difference-in-difference methods, fixed and random effects
- if time allows and according to students' interests: elementary "machine learning" techniques (decision trees, random forests, BARTs, shrinkage estimators, ...)

Lernergebnisse (Learning Outcomes)

After this course, students are able to critically discuss empirical studies using the econometric methods covered in this course. Moreover, students can independently plan, conduct, interpret, and present their own data analyses.

Regelung zur Anwesenheit

100% physical, emotional, and intellectual participation is strongly recommended in all sessions. Absence in a maximum of two sessions will be tolerated. Note that there will be no chance to make up for any points which were lost due to missing any of the sessions, failing to hand in a case study on time, missing the midterm exam, or not attending the final presentations.


In-class, content is presented using the whiteboard and presentation slides. Moreover, the methods are illustrated via case studies using R and EViews. To ensure the in-depth applicability of the material presented, students will work in groups on extensive case studies and on a project. The solutions to the case studies must be handed in as written reports. The project will be presented in form of an oral presentation during the last two sessions.

Leistung(en) für eine Beurteilung

The assessment is based on 3 components:

(1) Two case studies (10 points each, 20 points overall), accounts for 25% of the final grade
(2) Written exam in the antepenultimate session (30 points), accounts for 37.5% of the final grade
(3) Final presentation in the last two sessions (30 points), accounts for 37.5% of the final grade

The final grade will be determined as follows: 1 (at least 90%), 2 (at least 80%),  3 (at least 70%), 4 (at least 60%), 5 (less than 60%).


1 Autor/in: Jeffrey M. Wooldridge

Introductory Econometrics

Prüfungsstoff: Ja
Empfehlung: Referenzliteratur
Art: Buch
2 Autor/in: James H. Stock and Mark W. Watson

Introduction to Econometrics

Prüfungsstoff: Ja
Empfehlung: Referenzliteratur
Art: Buch

Empfohlene inhaltliche Vorkenntnisse

Successful completion of "Econometrics I" and "Econometrics II", i.e. an excellent conceptual and practical understanding of linear models and their statistical estimation (including common transformation of predictor and response variables, dummy coding of categorical predictors, models with trends and seasonalities, etc.), elementary time series econometrics (stationarity, common descriptive and inferential statistics for time series such as ACF, Durbin-Watson and Box-Ljung statistics, ARIMA models), and count / limited dependent variables models such as Poisson, negative binomial, logit, and probit regression. Working knowledge of R, EViews, or similar.

Zuletzt bearbeitet: 26.08.2020