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Course

Advanced Econometrics III


  • Teacher(s)
    Peter Boswijk, Siem Jan Koopman
  • Research field
    Econometrics
  • Dates
    Period 4 - Feb 27, 2023 to Apr 21, 2023
  • Course type
    Core
  • Program year
    First
  • Credits
    4

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. The second part focusses on state space models and the Kalman filter, discussing signal extraction, maximum likelihood estimation and dynamic factor models. The course will also discuss ARCH and score-driven volatility 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.