Description:
This course is a rigorous introduction to time series analysis with a focus on econometric applications. We begin by developing 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. We discuss state-space models and modern filtering and smoothing methods, in a frequentist and Bayesian setups. An introduction to dynamic factor models and simple models of financial time series rounds off the discussion of contemporary time series analysis.
Literature:
Brockwell, P.J., Davis R.A. (1991): Time Series: Theory and Methods, Springer.
Davidson, J. (2021): Stochastic Limit Theory (2nd ed), Oxford Univ Press.
Deistler, M., Scherrer, W. (2022): Time Series Models, Springer.
Hamilton, D.J. (1994). Time Series Analysis, Princeton University Press.
Neusser, K. (2016): Time Series Econometrics, Springer.
Särkkä, S., Svensson, L. (2023): Bayesian Filtering and Smoothing (2nd ed), Cambridge University Press.
Time & venue:
Lectures/ tutorials: tba
Exam:
Written exam
More information can be found on Moodle (no password).
