This course is a rigorous introduction to time series analysis with a focus on econometric applications. We begin with classical components models and we discuss how to treat seasonality. Then we develop in detail the theory of stationary processes with an emphasis on ARMA processes. We discuss the unit root methodology and procedures for forecasting as well as for the specification, estimation and validation of time series models. Multivariate extensions are demonstrated, with emphasis on (structural) vector autoregressive (VAR) processes and its application in causality and impulse response analyses. Nonstationary systems with integrated and cointegrated variables will also be treated. Finally, we discuss state-space models and modern filtering and smoothing methods, possibly in a Bayesian setup.
Hamilton, D.J. (1994). Time Series Analysis, Princeton University Press.
Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis, Springer Verlag, Heidelberg
Time & venue:
Lectures/ tutorials: tba
More information can be found on Moodle.