Moses - Microeconometrics

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Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum WS 2019/20 - WiSe 2023/24. Die Version für das aktuelle Semester (SoSe 2024) finden Sie hier: Modulbeschreibung des SoSe 2024

Module / Version:
#70187 / #4

Validity:
WS 2019/20 - WiSe 2023/24

Default display language:
English

Module title:
Microeconometrics

Credits:
6

Responsible person:
Werwatz, Axel

Grading:
benotet

Type of exam:
Portfolioprüfung

Zugehörigkeit

Faculty:
Fakultät VII

Institute:
Institut für Volkswirtschaftslehre und Wirtschaftsrecht

Area of expertise:
37312100 FG Ökonometrie und Wirtschaftsstatistik

Prüfungsausschuss:
Volkswirtschaftslehre

Kontakt

Office:
H 57

Contact person:
Hainbach, Tim Finn

E-mail address:
axel.werwatz@tu-berlin.de

Website:

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

Learning Outcomes

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.

Content

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.

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course Name	Type	Number	Cycle	Language	SWS	VZ
Microeconometrics	VL	71 210 L 1578	WiSe	English	2
Microeconometrics	UE	71 210 L 1579	WiSe	English	2

Workload and Credit Points

Microeconometrics (VL):

Workload description	Multiplier	Hours	Total
Class attendance	15.0	2.0h	30.0h
Class preperation and follow-up	15.0	2.0h	30.0h
			60.0h(~2 LP)

Microeconometrics (UE):

Workload description	Multiplier	Hours	Total
Class attendance	15.0	2.0h	30.0h
Class preperation and follow-up	15.0	1.0h	15.0h
			45.0h(~2 LP)

Course-independent workload:

Workload description	Multiplier	Hours	Total
Graded assignments	2.0	15.0h	30.0h
Preparation for the written exam	1.0	45.0h	45.0h
			75.0h(~3 LP)

The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

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.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Good understanding of the Topics of the Modul (70198) Ökonometrie. 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)

Mandatory requirements for the module test application:

1. Requirement

Modul70198 [Ökonometrie] passed

Module completion

Grading

graded

Type of exam

Portfolio examination

Type of portfolio examination

100 Punkte insgesamt

Language

English

Test elements

Name	Points	Categorie	Duration/Extent
Two written assignments	30	written	You 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 exam	70	written	75 Minutes

Grading scale

Notenschlüssel »Notenschlüssel 4: Fak I, Fak VII«

Gesamtpunktzahl	1.0	1.3	1.7	2.0	2.3	2.7	3.0	3.3	3.7	4.0
100.0pt	90.0pt	85.0pt	80.0pt	76.0pt	72.0pt	67.0pt	63.0pt	59.0pt	54.0pt	50.0pt

Test description (Module completion)

The portfolio examination consists of the following 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).

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Wintersemester.

Maximum Number of Participants

This module is not limited to a number of students.

Registration Procedures

Please note the information on our website.

Recommended literature
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.

Assigned Degree Programs

Semester:

Zeige auch ausgelaufene Studiengänge und StuPOs

This module is used in the following Degree Programs (new System):

	Studiengang / StuPO	StuPOs	Verwendungen	Erste Verwendung	Letzte Verwendung
This module is not used in any degree program.

Students of other degrees can participate in this module without capacity testing.

Miscellaneous

No information

Microeconometrics