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Advanced Econometrics III

  • Teacher(s)
    Peter Boswijk, Siem Jan Koopman
  • Research field
  • Dates
    Period 4 - Mar 04, 2024 to Apr 26, 2024
  • Course type
  • Program year
  • Credits

Course description

Several major advances in time-series econometrics and likelihood-based inference have occurred in the past years. These advances have provided a major breakthrough in the modeling of time series using advanced up-to-date econometric methodologies. The first part of the course aims to provide a thorough understanding of linear time series models, including frequency domain analysis, multivariate models and co-integration; it also covers GARCH models. The second part focusses on state space models and the Kalman filter, discussing signal extraction, maximum likelihood estimation and dynamic factor models. Various empirical illustrations in economics and finance will be discussed.

Course literature

Primary reading

  • Durbin, J. and Koopman, S.J. (2012). Time Series Analysis by State Space Methods, Second Edition, Oxford University Press
  • Van der Vaart, A.W. (2022). Statistical Time Series. Lecture notes, TU Delft (available via Canvas).

Further Reading

  • Brockwell, P.J. and Davies, R.A. (1987). Time Series: Theory and Methods, New York: Springer-Verlag
  • Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge University Press
  • Shumway, R.H. and Stoffer, D.S. (2000). Time Series Analysis and Its Applications, New York: Springer Verlag.