This course covers theoretical and practical aspects of time series econometrics including the analysis of stationary and non-stationary stochastic processes.
The students are introduced to concepts in the statistical analysis of time series, dynamic processes, autoregressive moving average (ARMA) models, autoregressive distributed lag (ADL) models, error correction models (ECM), and generalized autoregressive conditional heteroscedasticity (GARCH) models. Furthermore, the course provides both theoretical and practical insights into parameter estimation for time series models and the use of these models for forecasting, testing for Granger causality, and performing policy analysis using impulse response functions.
Finally, the students are introduced to the fundamental problem of spurious regression in time series analysis. In order to find a solution to this problem, we take a journey into the wonderful world of the theory and practice behind unit-root test, co-integration tests and error-correction representation theorems.
All the relevant material for the exam can be found in the lecture slides and will be discussed in class.