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Program

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

3. Estimation

  • Maximum likelihood
  • Bayesian estimation (Simulation inference: the simulation smoother)
  • DSGE example. Time-Varying Parameters VAR example

References

  • 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.