DE

Modul

Econometrics and Economics [M-WIWI-101420]

Credits
9
Recurrence
Jedes Semester
Duration
2 Semester
Language
Level
3
Version
3

Responsible

Organisation

  • KIT-Fakultät für Wirtschaftswissenschaften

Part of

Bricks

Identifier Name LP
T-WIWI-102792 Decision Theory 4.5
T-WIWI-102844 Industrial Organization 4.5
T-WIWI-103063 Analysis of Multivariate Data 4.5
T-WIWI-103065 Statistical Modeling of Generalized Regression Models 4.5

Competence Certificate

The module examination is carried out in the form of partial examinations on the selected courses, with which the minimum LP requirement is fulfilled in total.

The assessment of success is described for each course.

The overall grade of the module is formed from the LP-weighted grades of the partial examinations and truncated after the first decimal place.

Competence Goal

The student

  • Knows and understands the common statistical or econometric methods in the fields of quantitative finance for financial institutions,
  • knows and understands the modern risk control or analysis methods,
  • knows and understands the presentation of axiomatic decision theories, stochastic dominance principles or risk aversion concepts.

Prerequisites

Courses audited in connection with this module can no longer be credited in connection with modules from the master's program.

Content

Industrial Economics:

  • Hold-Up Problem (motivation and model)
  • Wrap-Up: Introduction (History)
  • Asymmetric Information
  • Welfare analysis
  • Market structures
  • Barriers to entry
  • Monopoly
  • Welfare analysis
  • Price discrimination
  • Oligopoly: Cournot model and competitive intensity
  • Stackelberg model (sequential quantity competition)
  • Bertrand model
  • GWB, obstacles to competition
  • Merger
  • Tacit Collusion
  • Modeling of product differentiation
  • Exogenous and Endogenous Product Differentiation
  • Monopolistic competition (product variety)

Statistical modeling of general regression models:

The basic aim of the lecture will be to introduce regression techniques as a central tool of statistical modeling.

  • Introduction and topic overview,
  • Model classes in statistical analysis and model fitting,
  • Generalized Linear Models,
  • Multiple Linear Regression,
  • Logistic Regression,
  • Nonparametric Regression,
  • Introduction Survival Time Analysis.


Analysis of Multivariate Data:

  • Mathematical and statistical foundations for the analysis of multivariate data.
  • Data inspection and pre-treatment
  • Data structure analysis and reduction
  • (Supervised) data analysis models
  • Data model validation


Decision Theory:

  • Decision under uncertainty
  • Expected utility theory for risk decisions
  • Risk measurement
  • Stochastic Dominance
  • Prospect Theory
  • Personal equilibrium
  • Ambiguity
  • Epistemology

Workload

The total workload for this module is approximately 270 hours.