ADVANCED TOPICS IN TIME SERIES
Updated A.Y. 2022-2023
This module aims at providing a sound knowledge of the filtering theory and the estimation of macroeconomic models. It is divided into two parts: the first one deals with filtering theory whereas the second discusses the Bayesian estimation of the macro models.
1. Filtering theory Part 1
- Linear filters
- Gain and phase
2. Filtering theory Part 2
- State-space form
- Kalman filter and Smoother
- Some economic examples
- Maximum likelihood
- Bayesian estimation (Simulation inference: the simulation smoother)
- DSGE example. Time-Varying Parameters VAR example
- Durbin, J., and Koopman, S.J. (2001), Time Series Analysis by State Space Methods, Oxford University Press, Oxford, UK.
- Harvey, A.C. (1989), Forecasting, Structural Time Series and the Kalman Filter, Cambridge University Press, Cambridge, UK.