On completion of the module, students will have learnt how to construct regression models in a structured way and will be able to explain the relationships between economic variables. They will be able to empirically estimate model parameters and interpret them. In addition, they will be aware that estimates are always based on certain assumptions, which must always be questioned and, if possible, tested, and that the type of data available has a significant influence on the interpretation of the estimation results as well as on the choice of estimation methods. Students have familiarized themselves with a statistical software package.

Introduction to econometric modelling, linear regression and least squares estimation, measures of quality, testing hypotheses in a regression context, variable selection, ranges and functional form, multicollinearity and model plausibility, lasso and ridge regression, heteroskedasticity and weighted least squares estimation, autocorrelation and generalized least squares estimation, robust standard errors, endogeneity and instrumental variable estimation also using the control function approach, basics of panel data and fixed effects and first difference estimation, time series data with stationarity, and error correction plots.

All content is covered both theoretically and with concrete implementation in [R].

You can find the complete module description here.