Facoltà di Economia

Aggiornato A.A. 2021-2022

Filtering

Linear filter
State-space form
Kalman filter and Smoother
Some economic examples
Estimation

Maximum likelihood
Bayesian estimation
Economic examples

  Bayesian VAR
  Time Varying Parameters VAR
   Large VAR

Big data in economics

Macroeconomic modelling and forecasting.

 

  

 

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.
West, M., and Harrison, J. (1997), Bayesian Forecasting and Dynamic Models, 2nd ed., Springer-Verlag, New York.
Kim, C.J., and Nelson, C. R. (1999),  State-Space Models with Regime-Switching. Cambridge MA: MIT Press.
Shumway, R.H., and Stoffer, D.S. (2000), Time Series Analysis and Its Applications, Springer-Verlag, New York.
Cappé, O., Moulines, E., and Rydén, T. (2005). Inference in hidden markov models. Springer Series in Statistics. Springer, New York.
Fruehwirth-Schnatter, S. (2006). Finite Mixture and Markov Switching Models.Springer Series in Statistics. Springer, New York.
Hamilton, J. (1994). Time Series Analysis. Princeton University Press.
Proietti, T. (2008). Structural Time Series Models for Business Cycle Analysis. In the Handbook of Econometrics: Vol. 2, Applied Econometrics, Part 3.4., ed. T. Mills and K. Patterson, Palgrave, London.

Aggiornato A.A. 2016-2017

The course will cover three main topics

1. State space models, Maximum likelihood estimation, Univariate and Multivariate Stochastic Volatility
2.  Forecast evaluation: Introduction: schemes, number of observations, in vs out of sample, measures of accuracy (MSFE, MAFE, forecast encompassing).
3. MIDAS: MIxed DAta Sampling. Introduction. Forecasting with mixed (and high) frequency data.

References 

  The main references will be given during course and the relevant papers will be uploaded in the Teaching material section