Content:
1. Motivation
2. The Simple Regression Model ( OLS: assumptions, model and
estimator, Goodness-of-Fit, Statistical properties of the OLS estimator)
3. The Multiple Regression Model (Model, Interpretation of
coefficients, Gauss-Markov-Theorem)
4. Inference & Hypothesis Testing (Testing a single parameter: the
t-Test, Testing a linear combination of parameters, Testing multiple
linear restrictions: the F-Test, Confidence intervals, OLS asymptotics)
5. Heteroscedasticity and Autocorrelation
6. Maximum-Likelihood-Estimation (The Likelihood function, The ML
estimator, Properties)
- Kursverantwortliche/r: Vahidin Jeleskovic
- Kursverantwortliche/r: SHK Xiaohao Ji
- Kursverantwortliche/r: Kejsi Progni
Semester: WiSe 2022/23