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Mikroökonometrie

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#70187 / #5

Seit SoSe 2024

English

Microeconometrics
Mikroökonometrie

6

Werwatz, Axel

Benotet

Portfolioprüfung

English

Zugehörigkeit


Fakultät VII

Institut für Volkswirtschaftslehre und Wirtschaftsrecht

37312100 FG Ökonometrie und Wirtschaftsstatistik

Volkswirtschaftslehre

Kontakt


H 57

Hainbach, Tim Finn

axel.werwatz@tu-berlin.de

http://www.statistik.tu-berlin.de

Lernergebnisse

Microeconometrics is the collection of models and methods for analyzing data on individuals (micro data). Such data typically contains information about the choices and outcomes of individual economic agents such as people, households, plants or firms. Choice variables of individuals are often non-standard from a statistical point of view: they may be qualitative (indicating the chosen alternative), discrete (counts) or limited (truncated, censored; e.g. spending on durable goods is often zero and otherwise positive). In such cases, the standard linear regression model is no longer adequate. A range of nonlinear econometric models has been developed to analyze this data (Logit-, Probit-, Tobit models). The aim of the course is to give the students a solid introduction into formulation, estimation and interpretation of these models. A key ingredient of the course are the weekly tutorials that focus on hands-on experience in applying these methods to actual data accompanied by 2-3 compulsory homework assignments. The tutorial introduces students to the free statistical software R which is also a preferable tool for the completion of the assignments. At the end of the course, students can be expected to be able to carry out basic data analysis of the relevant questions on their own.

Lehrinhalte

Maximum Likelihood Estimation and Inference, Discrete Response Models (Probit, Logit, Ordered Probit, Multinomial Logit and Probit), Regression models for censored and (incidentally) truncated dependent variables (Tobit, Heckit), Count data regression (Poisson Regression). Weekly tutorials focus on hands-on experience in applying these models and methods to actual or simulated data using the free statistical software R. Due to capacity constraints, students are encouraged to bring their laptops to the exercises. Having said that, own computer in the tutorials is no prerequisite of the course; all materials will be uploaded for an easy access at home. Students are thus able to study and rerun everything on their home computers or in the computer lab.

Modulbestandteile

Compulsory area

Die folgenden Veranstaltungen sind für das Modul obligatorisch:

LehrveranstaltungenArtNummerTurnusSpracheSWS ISIS VVZ
MicroeconometricsVL71 210 L 1578WiSeen2
MicroeconometricsUE71 210 L 1579WiSeen2
ISIS-Kurssuche nach Veranstaltungen des Moduls

Arbeitsaufwand und Leistungspunkte

Microeconometrics (VL):

AufwandbeschreibungMultiplikatorStundenGesamt
Class attendance15.02.0h30.0h
Class preperation and follow-up15.02.0h30.0h
60.0h(~2 LP)

Microeconometrics (UE):

AufwandbeschreibungMultiplikatorStundenGesamt
Class attendance15.02.0h30.0h
Class preperation and follow-up15.01.0h15.0h
45.0h(~2 LP)

Lehrveranstaltungsunabhängiger Aufwand:

AufwandbeschreibungMultiplikatorStundenGesamt
Graded assignments2.015.0h30.0h
Preparation for the written exam1.045.0h45.0h
75.0h(~3 LP)
Der Aufwand des Moduls summiert sich zu 180.0 Stunden. Damit umfasst das Modul 6 Leistungspunkte.

Beschreibung der Lehr- und Lernformen

Lecture and Tutorial. In the lecture, intuitive understanding of the mechanics at work in these models is developed. Considerable amount of time is spend on understanding the maximum likelihood estimation approach as well as on the discrete choice models to give students a sound basis for more complicated models. In the tutorial, the models are applied to real or simulated data using R, which is a free software environment for statistical computing. In this way, students can occasionally prepare or rerun the analysis either at home or in the university computer lab facilitating their understanding. Another crucial part of the course are the compulsory homework assignments. Students are asked to apply the theoretical concepts from the lecture using the tools and practical understanding acquired in the exercises to carry out limited in complexity yet practical analysis. An introduction to R will be given at the beginning of the course.

Voraussetzungen für die Teilnahme / Prüfung

Wünschenswerte Voraussetzungen für die Teilnahme an den Lehrveranstaltungen:

Good understanding of the Topics of the Modul (70198) Ökonometrie is highly recommended! most notably: Understanding the concepts of Bias and Variance of an Estimator Regression analysis (Ordinary Least Squares - multivariate Regression) and related topics Hypothesis testing (t-Test, F-Test)

Verpflichtende Voraussetzungen für die Modulprüfungsanmeldung:

Dieses Modul hat keine Prüfungsvoraussetzungen.

Abschluss des Moduls

Benotung

Benotet

Prüfungsform

Portfolio examination

Art der Portfolioprüfung

100 Punkte insgesamt

Sprache(n)

English

Prüfungselemente

NamePunkteKategorieDauer/Umfang
Two written assignments30schriftlichYou will have to successfully solve 2 written assignments. Both involve programming tasks in R as well as theoretical questions (e.g.: interpreting results or explaining concepts introduced in the lecture). You will work in groups consisting of 2-3 students.
Written exam70schriftlich60 Minutes

Notenschlüssel

Notenschlüssel »Notenschlüssel 4: Fak I, Fak VII«

Gesamtpunktzahl1.01.31.72.02.32.73.03.33.74.0
100.0pt90.0pt85.0pt80.0pt76.0pt72.0pt67.0pt63.0pt59.0pt54.0pt50.0pt

Prüfungsbeschreibung (Abschluss des Moduls)

The portfolio examination consists of the previously mentioned elements, adding up to a maximum of 100 credits. The grading follows the joint conversion key of the School of Economics and Management (decision of the school's council dated May 28, 2014 - FKR VII-4/8-28.05.2014).

Dauer des Moduls

Für Belegung und Abschluss des Moduls ist folgende Semesteranzahl veranschlagt:
1 Semester.

Dieses Modul kann in folgenden Semestern begonnen werden:
Wintersemester.

Maximale teilnehmende Personen

Dieses Modul ist nicht auf eine Anzahl Studierender begrenzt.

Anmeldeformalitäten

Please note the information on our website.

Literaturhinweise, Skripte

Skript in Papierform

Verfügbarkeit:  nicht verfügbar

 

Skript in elektronischer Form

Verfügbarkeit:  verfügbar
Zusätzliche Informationen:

 

Literatur

Empfohlene Literatur
Baum, C.F. (2006) An Introduction to Modern Econometrics Using STATA, Stata Press
Cameron, A.C. and Trivedi, P.K. (2005). Microeconometrics: Methods and Applications, Cambridge University Press.
Fahrmeir, L. und Tutz, G. (2001), Multivariate Statistical Modelling Based on Generalized Linear Models, 2nd edition, Springer
Goldberger, A.S. (1991). A Course in Econometrics, Harvard University Press Greene, W. (2003). Econometric Analysis, 5e, Prentice Hall.
Härdle, W., Müller, M., Sperlich, S. und Werwatz, A. (2004), Nonparametric and Semiparametric Models, Springer Verlag
Long, J. S. und Freese, J. (2006), Regression Models for Categorical Dependent Variables Using STATA, 2nd Edition, Stata Press
Verbeek, M. (2004). A Guide to Modern Econometrics, 2e. John Wiley & Sons.
Winkelmann, R. und Boes, S. (2006) Analysis of Microdata, Springer Verlag
Wooldridge, J.M. (2001). Econometric Analysis of Cross Section and Panel Data, MIT Press.
Wooldridge, J.M. (2006). Introductory Econometrics. A Modern Approach, 3e, Thomson South-Western.

Zugeordnete Studiengänge


Diese Modulversion wird in folgenden Studiengängen verwendet:

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Economics and Sustainability (M. Sc.)12WiSe 2024/25SoSe 2025
Industrial Economics (M. Sc.)13SoSe 2024SoSe 2025
Information Systems Management (Wirtschaftsinformatik) (M. Sc.)13SoSe 2024SoSe 2025
Soziologie technikwissenschaftlicher Richtung (B. A.)13SoSe 2024SoSe 2025
Wirtschaftsingenieurwesen (M. Sc.)13SoSe 2024SoSe 2025

Studierende anderer Studiengänge können dieses Modul ohne Kapazitätsprüfung belegen.

Sonstiges

Keine Angabe