Regression and Time Series Analysis
This course focuses on regression models for cross-sectional as well as time series data. It offers an in-depth discussion of the fundamental multiple linear regression model for cross-sectional data and of methods that aim at modeling the dynamic behavior of economic variables. The latter is especially relevant to quantify the effect of past shocks or changes of variables onto the current or future value of a specific economic variable and to compute forecasts by exploiting this correlation over time. Typical examples are forecasts of macroeconomic variables, such as GDP or inflation, as well as price and volatility forecasts of financial assets, which are essential components for adequate risk management and portfolio allocation decisions.
Particular focus will be given on model specification, as well as on the interpretation, consistent estimation, and hypothesis testing of the parameters of the respective models. Methods to assess the fit of the model and its forecast accuracy will also be covered.
Familiarity with the simple linear regression model on the level of an undergraduate course in Econometrics is recommended.
Alternatively, the Refresher Course in Basic Statistics and Econometrics can be attended, which is usually offered as a block course at the beginning of the semester.
Course language: English
Lecture (4 SWS):
October 13, 2022
Thursdays, 02:00-04:00pm, M.14.25
Fridays, 08:00-10:00am, M.14.25
cf. StudiLöwe WIR022203
Exercises (2 SWS):
Tuesdays, 12:00am-02:00pm, M.15.13, cf. StudiLöwe WIR022204
October 18, 2022