Time Series Analysis

 The focus is on building a strong formal familiarity with models for time series analysis. The lecture begins with classical components models. Then we cover different types of stochastic processes like ARIMA and GARCH models, deal with the unit root methodology and procedures for forecasting as well as for the specification, estimation and validation of models. Multivariate extensions are demonstrated, with emphasis on vector autoregressive (VAR) processes and its application in causality and impulse response analyses. Nonstationary systems with integrated and cointegrated variables will also be treated. If time permits, we also discuss state space models and various modern methods to deal with high-dimensional time series (e.g., factor models, Bayesian filtering and smoothing).