Institution: Graduate School at Faculty of Business, Economics and Social Sciences – Universität Hamburg

Lecturer: Prof. Dr. Miriam Beblo, Prof. Dr. Jan Marcus (WiSO-Fakultät, UHH)

Schedule: mondays, 16:30 – 18:00 (starting 24.10.2016, bi-weekly, plus on day in January)

Place: Universität Hamburg, further information in Geventis

Registration: You can register for the course until 30.09.2016 (13 Uhr) via Geventis

Course description:
The course Empirical Labour Economics consists of two parts, a seminar part and a one-day workshop.

Seminar part: Participants are expected to regularly attend the researchseminar Labour Economics, which is jointly organized by the departments ofSocioeconomics and Economics as well as external research institutions andwhere external speakers are invited to present and discuss their current workon labour, family and migration issues. The seminar is held biweekly on Mondaysfrom 4.30 to 6 pm (for the programme see http://www.wiso.uni-hamburg.de/fachbereich-sozoek/professuren/beblo/04-forschung/forschungsseminar.html). Participants should hand in a written comment on one of the seminartalks (focusing on research design and implementation).

Workshop part: Toward the end of the semester the course concludes witha day-long meeting, where all participants will present their own researchideas and implementation problems, in consideration of the labour seminarpapers discussed.

Institution: Graduate School at Faculty of Business, Economics and Social Sciences – Universität Hamburg

Lecturer: Prof. Dr. Ulrich Fritsche (WiSo-Fakultät, UHH)

Schedule:
Di., 21.02.2017, 09:00 – 17:00 Uhr
Mi., 22.02.2017, 09:00 – 15:00 Uhr
Do., 23.02.2017, 09:00 – 17:00 Uhr
Fr., 24.02.2017, 09:00 – 15:00 Uhr

Place: Universität Hamburg, further information in Geventis

Registration: You can register for the course until 30.09.2016 (13 Uhr) via Geventis

Course description:
Roadmap:

1. Basics: Difference Equations, Solutions, Lag Operators
2. Stationary Time-Series Models: ARMA (p,q), ACF/PACF, Box-Jenkins
3. Identification Problems in Macroeconometrics
4. Models with Trend: Dickey-Fuller-Test, Structural Change, Panel UnitRoot tests
5. Cointegration and Error-Correction Models
6. Some Non-linear Time-Series Models

Lernziel:

Students will be enabled to apply macro econometric techniques to avariety of cases. Students are encouraged to bring their own problems and data sets to analyze them in the course.Students will be enabled to use the software RATS.

Vorgehen:

The course is a mixture of lectures, practical exercises and programmingRATS code and own empirical work.

Literatur:

Basic References:

• @1: Enders (2010), ch. 1;Kirchgässner, Wolters (2007), ch. 1.
• @2: Enders (2010), ch. 2;Kirchgässner, Wolters (2007), ch. 2.
• @3: Favero (2001), ch. 3, ch.4, ch. 6; Kirchgässner, Wolters (2007), ch. 4; Enders (2010)3, ch. 5.
• @4: Enders (2010), ch. 4;Kirchgässner, Wolters (2007), ch. 5.
• @5: Enders (2010), ch. 6;Kirchgässner, Wolters (2007), ch. 6.
• @6: Enders (2010), ch. 7.

Books:

• Walter Enders (2010): Applied Econometric Time Series, 3rd edition,Wiley.
• Carlo A. Favero (2001): Applied Macroeconometrics, Oxford UniversityPress.
• Gebhard Kirchgässner, JürgenWolters (2007): Introduction to Modern Time Series Analysis, Springer.
• RATS: www.estima.com

Zusätzliche Hinweise zu Prüfungen:

Students will work on empirical projects (either own projects or tasks defined in the course). A written documention of the empirical project will be graded.

Institution: Graduate School at Faculty of Business, Economics and Social Sciences – Universität Hamburg

Lecturer: Prof. Dr. Katharina Kleinen-von Königslöw (WiSo-Fakultät, UHH)

Schedule:
Do., 24.11.2016, 09:00 – 17:00 Uhr
Fr., 25.11.2016, 09:00 – 15:00 Uhr
Do., 12.01.2017, 09:00 – 17:00 Uhr
Fr., 13.01.2017, 09:00 – 15:00 Uhr

Place: Universität Hamburg, further information in Geventis

Registration: You can register for the course until 30.09.2016 (13 Uhr) via Geventis

Course description:
Für eine Vielzahl sozialwissenschaftlicher Fragestellungen bleibt die manuelle quantitative Inhaltsanalyse weiterhin die Methode der Wahl, weil sie die systematische Erhebung komplexerer Konzepte – wie etwa Frames, politische Claims, Diskursqualität (Deliberation) oder Nachrichtenwerte –erlaubt, die derzeit (noch?) nicht verlässlich automatisiert erfasst werden können. Ziel des Workshops ist es zunächst zu erarbeiten, wie diese Konzepte valide und reliabel mittels manueller Inhaltsanalysen erfasst werden können. Im zweiten Block steht die Auswertung inhaltsanalytischer Daten im Vordergrund,die in der statistischen Grundausbildung oft zu kurz kommt: Jenseits vonKreuztabellen, wie lassen sich z.B. Faktoren- und Clusteranalysen für dieAuswertung von Frames oder Netzwerkanalysen für die Auswertung politischerClaims nutzbar machen?

Die Veranstaltung richtet sich an Doktorierende, die einemanuelle quantitative Inhaltsanalyse von Print-, Online-, und sozialen Medienoder vergleichbaren Textsorten planen oder aber noch Anregungen für dieAuswertung ihrer vorliegenden Inhaltsanalyse-Daten suchen. Gute Kenntnisse eines statistischen Auswertungsprogramms werden dringend empfohlen, die Arbeit im Kurs erfolgt in R und/oder SPSS. Je nach Interessenslage derKursteilnehmerInnen kann auch auf Fragen der Datenakquise (Datenbanken,API-Schnittstellen etc.), der für die Codierung verfügbaren Software oder der ergänzenden (semi-)automatisierten Inhaltsanalyse besprochen werden.

Institution: Graduate School at Faculty of Business, Economics and Social Sciences – Universität Hamburg

Lecturer: Prof. Dr. Olaf Posch (WiSo-Fakultät, UHH)

Schedule: Freitags, 09:00 – 12:00 Uhr (ab 11.11.2016) wöchentlich

Place: Universität Hamburg, further information in Geventis

Registration: You can register for the course until 30.09.2016 (13 Uhr) via Geventis

Course description:
This course provides a toolbox for solving dynamic optimization problems in stochastic macroeconomic models. In particular, we briefly review optimal control theory and dynamic programming. We then thoroughly study models in discrete time and continuous time under uncertainty. The optimization problems are illustrated by various examples of dynamic stochastic general equilibrium (DSGE) models.

Course outline.

Part I: Basic mathematical tools

(i) Control theory (maximum principle, Euler equation, transversality condition)

(ii) Dynamic programming (Bellman equation, envelope theorem, multiple variables)

(iii) An example: Lucas’ model of endogenous growth

Part II: Stochastic models in discrete time

(i) Stochastic control problems

(ii) Analyzing equilibrium dynamics

(iii) An example: Real business cycles (RBC)

(iv) An example: A new Keynesian model for monetary analysis

(v) Solving dynamic equilibrium models with Dynare

Part III: Stochastic models in continuous time

(i) Stochastic differential equations and rules for differentials (Itˆo’s formula)

(ii) An example: Merton’s model of growth under uncertainty

(iii) Stochastic dynamic control problems (Bellman equation)

(iv) An example: Continuous-time RBC (under Gaussian and/or Poisson uncertainty)

(v) An example: The matching approach to unemployment

(vi) An example: Walde’s model of endogenous growth cycles

References

• Chang, F.-R. (2004): Stochastic optimization in continuous time. Cambridge Univ. Press.
• Sydsæter, K., P. Hammond, A. Seierstad, and A. Strøm (2008): Further Mathematics for Economic Analysis. Prentice Hall.
• Walde, K. (2012): Applied Intertemporal Optimization. Lecture Notes, Gutenberg University Mainz, http://www.waelde.com/aio.