The students acquire and deepen their knowledge of statistics, econometrics and optimization. In each case, selected focal points of the topics are discussed theoretically in depth and analyzed, discussed and calculated using examples. After participating in the module, students are able to fundamentally understand statistical estimation and optimization methods, apply them to different fields of economics (e.g. innovations), and critically question their assumptions and test properties analytically and numerically. Thus, upon completion of the module, students will have both an understanding of mathematical principles and the application of mathematical content in an economic context.

Optimization (25%):

• Analytical nonlinear optimization using the Lagrange method as an example
• Numerical methods using the example of Regula-Falsi and Newton method
• Stochastic methods using the example of Simulated Annealing
• Distributed optimization on the example of ant models and social learning

Statistics (25%):

• Estimation and proberties estimators (e.g. MSE, consistency, unbiasedness)
• Testing and properties of tests (e.g. goodness functions, power, and unbiasedness)?
• Selected additional statistics and tests (e.g. meta-analysis, Hausmann test)?

Econometrics (50%):

• Basic estimation methods, e.g. Maximum-Likelihood, Bayesian, quantile regression, GMM
• General linear model: basic idea and linear as well as binary logistic and Poisson regression as example as well as selected extensions
• Survival models

In the exercise, the topics of the lecture are are deepened by means of exercises. In addition to the theory, sample calculations are provided online for the statistical software package [R].

You can find the complete module description here.