Institution: Graduate School at Faculty of Economics and Social Sciences – University of Hamburg

Lecturer: Vertr.-Prof. Dr. Daniela Schiek

Schedule:
Einführungstermin: Fr., 13.11.15, 14:00 – 16:00 Uhr
Fr., 27.11.15, 10:00 – 16:00 Uhr
Sa., 28.11.15, 10:00 – 16:00 Uhr
Fr., 11.12.15, 10:00 – 16:00 Uhr
Sa., 12.12.15, 10:00 – 16:00 Uhr

Place: University of Hamburg, Von Melle Park 5

Registration: Anmeldungen sind ab sofort bis zum 15.10.2015 (13:00 Uhr) über Geventis möglich.

Course description:
In dieser Veranstaltung soll (eigenes) Fallmaterialgemeinsam ausgewertet werden, wobei hermeneutische Einzelfallrekonstruktionenund fallkontrastierende Kodier-Verfahren (Grounded Theory) angewendet werden.In einer Einführungssitzung werden spezifische Fragen, Vorgehen im Workshopsowie dessen (individuelle) Vorbereitung besprochen und miteinander abgestimmt.

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Institution: Graduate School at Faculty of Economics and Social Sciences – University of Hamburg

Lecturer: Prof. Dr. Olaf Posch

Schedule:
Friday, 16.10.15 – Friday, 18.12.15
weekly 09:00 – 12:00

Place: University of Hamburg, Von Melle Park 9

Registration: You can register for the course until 15.10.2015 (13:00) via Geventis.

Course description:
Course objective. 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

Reading list. Sydsæter, Hammond, Seierstad, and Strøm (2008, chap. 4-12, 290 pages), Chang (2004, chap. 4, 50 pages), Walde (2012), various articles suggested as complementary material during the course

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.

Further information

Institution: Graduate School at Faculty of Economics and Social Sciences – University of Hamburg

Lecturer: Prof. Dr. Dr. Lydia Mechtenberg

Schedule:
Thursday, 22.10.15 – Thursday, 28.01.16
every two week 10:00 – 13:00

Place: University of Hamburg, Von Melle Park 9

Registration: You can register for the course until 30.09.2015 (13:00) via Geventis.

Course description:
Topics: This course offers a new perspective on Political Economics by informingthe latter field with insights from Behavioral Economics. Political Economicstraditionally analyses social choice, voting, lobbying, political institutionsand government behavior under standard assumptions. Behavioral Economics, bycontrast, identifies open puzzles in Political Economics and explains them withthe help of insights from Behavioral Economics, like bounded rationality(illusion of control, incorrect probability judgments, limited memory etc.) andsocial and / or procedural preferences. These new theories are tested in thelab.

Method: Students present and discuss recent research in BehavioralPolitical Economics in class and provide a “referee report” on a related paper.

Aim: The goal of this course is twofold. First, students will become“competent consumers” of the literature in Behavioral Economics. Second, theywill be enabled to develop a research project in this field that may becomepart of their dissertation.

Organization: A reading list will be provided by the beginning of October. Inthe first session on class, I will shortly present the papers, and the studentswill choose what to present and what paper to write a report on. Then, we willmeet every other week for the presentation and discussion of the literature.

Requirements: PhD Students who want to participate must have taken some coursein Behavioral or Experimental Economics. The class is open to advanced Masterstudents.

Further information

Institution: Graduate School at Faculty of Economics and Social Sciences – University of Hamburg

Lecturer: Dr. Daniel R. Hawes

Schedule:
Thursday, 13.10.15 – Thursday, 26.01.16
Weekly 10:00 – 12:00

Place: University of Hamburg, Von Melle Park 9

Registration: You can register for the course until 30.09.2015 (13:00) via Geventis.

Course description:
R is a statistical computing environment. Mastering the R language means becoming proficient in state-of-the art software used to organize, understand, and explain data. R is a freely available open-source program, and is increasingly used in academia as well as industry: Indeed, mid through 2015, R stands as the world’s 6th most used programming language (not just statistics!) [ see: IEEEspectrum.org ].

R is powerful, flexible, and rapidly advancing. This progress results in large parts from the activity of a dynamic and active community of developers, statisticians, and scientists who work with data. As a byproduct, social science PhD students who become proficient in R will not only find their elementary scientific computing needs met within a single programming language, but will simultaneously benefit from generously available online support regarding many questions that arise while learning to code and to generally “work with data”.

The goal for this seminar is to equip graduate students with a bird’s-eye view of the global R environment. This means that students will learn the larger landscape of tools that exist in R and be introduced to how these tools can be utilized to efficiently streamline data-aspects of scientific research.

No prior knowledge of R is required. Several homework sets will be provided from which students can develop familiarity with basic R syntax, and a brief introduction to R and RStudio will be covered at the beginning of the course. The course is not a statistics course, and lessons will emphasize the development of a general overview regarding powerful data handling tools available in R and how to utilize these in research.

Of central importance to the course will be the treatment of relatively new R packages for handling data (e.g. dplyr & magrittr), packages to create nifty graphics (ggplot2 & ggvis), as well as various tools that assist in proper documentation and convenient presentation of analysis (e.g. tidyr, knitr, & shiny).

The course is conceived as a graduate seminar for credit. Students from the Social Sciences, Psychology, and Economics are primarily addressed, moreover, intended participants should be actively engaged in ongoing research. Students will be given the opportunity to present on particular features of R, relevant to their field.

Further information

Institution: Graduate School at Faculty of Economics and Social Sciences – University of Hamburg

Lecturer: Prof. Dr. Ulrich Fritsche

Schedule:
Thursday, 14.01.16, 14:00-18:00
Friday, 15.01.16, 10:00-18:00
Thursday, 21.01.16, 14:00-18:00
Friday, 22.01.16, 10:00-18:00

Place: University of Hamburg, Von Melle Park 9

Registration: You can register for the course until 30.11.2015 (13:00) 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 Unit Root tests
5. Cointegration and Error-Correction Models
6. Some Non-linear Time-Series Models

Students will be enabled to apply macroeconometric techniques to a variety 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.

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

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 University Press.
Gebhard Kirchgässner, Jürgen Wolters (2007): Introduction to Modern Time Series Analysis, Springer.

RATS: www.estima.com

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

Further information

Institution: Graduate School at Faculty of Economics and Social Sciences – University of Hamburg

Lecturer: Thorsten Dresing hat in Marburg Pädagogik und Soziologie studiert und an der Philips-Universität Marburg zum Thema „Entwicklung und Evaluation eines hybriden Onlineseminars zur Textanalyse“ promoviert. Hauptberuflich ist er geschäftsführender Gesellschafter der dr. dresing & pehl GmbH – audiotranskription.de und entwickelt die Transkriptions- und QDA-Software f4 und f4analyse. Freiberuflich ist er seit 14 Jahren Dozent für qualitative Sozialforschung mit dem Fokus auf f4, MAXQDA und qualitativer Inhaltsanalyse.

Schedule:
Do., 19.11.15, 09:00 – 15:30 Uhr
Fr., 20.11.15, 09:00 – 15:00 Uhr

Place: University of Hamburg, Von Melle Park 9

Registration: Anmeldungen sind ab sofort bis zum 15.10.2015 (13:00 Uhr) über Geventis möglich.

Course description:
In diesem Kurs erhalten Sie eine vertiefte Einführung in die Transkription von Interviews, die Aufbereitung anderer Datenarten für und den Umgang mit dem qualitativen Datenanalyseprogramm MAXQDA. Der Fokus des Kurses liegt, neben dem Codieren und Arbeiten mit Memos, auf der Umsetzung von mixed methods Ansätzen im Rahmen wissenschaftlicher Forschungsprojekte. Dieser Kurs vermittelt, welche Funktionen MAXQDA offeriert und wie sich diese im Forschungsprozess für unterschiedlich methodische Vorgehensweise sinnvoll und pragmatisch einsetzen lassen. Es werden Fragen beantwortet wie:

• Wie verschrifte ich meine Interviews regelgeleitet und synchronisiere die
  Aufnahmen mit den Transkripten?
• Wie entwickle ich ein Kategoriensystem induktiv oder deduktiv, ordne Textstellen
  passend zu und differenziere das Codesystem aus?
• Wie nutze ich Memos als Forschungswerkzeug sinnvoll für Interpretationsansätze,
  Case Summarys, Ankerbeispiele, Definitionen und Theorieideen?
• Wie kann ich meine Analysedurchgänge mit dem Text-Retrieval und Suchwerkzeug
  gestalten und ggf. interessante Passagen automatisch codieren?
• Wann helfen Variablen bei der qualitativen Datenanalyse und wie lassen sich
  selektive Aussagen von bestimmten Personengruppen herausfiltern, um sie bspw. zu
  kontrastieren?
• Wie visualisiere ich meine Daten für die Auswertungsphase und den
  Ergebnisbericht?
• Wie gestalte ich die Arbeit im Team?

Zielgruppe: Dieser Workshop richtet sich an Studierende, Promovierende und ProjektmitarbeiterInnen aus den sozialwissenschaftlichen Disziplinen sowie an alle, die sich für die Auswertung qualitativer Daten interessieren. Vorkenntnisse zu MAXQDA werden nicht vorausgesetzt.

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