PhD Courses in Denmark

The course is split into 2 modules:

Module one (3 weeks) covers probability theory and asymptotic analysis of extremum estimators and statistical tests. The class of extremum estimators includes but is not limited to maximum likelihood, (nonlinear) least squares and generalized method of moments, which may all be defined as solutions to maximisation/minimisation problems.

The second module (4 weeks) of the course is about asymptotic theory for nonparametric, semiparametric, and machine learning estimators. Examples include, but are not limited to, kernel methods for densities and regression, generalized random forests, and deep neural networks. It also covers semiparametric inference using machine learning and other methods for structural parameters, causal inference, missing data, and heterogeneity analysis. 

Both modules include programming applications.