Now showing items 6-10 of 10

    • Robust time series models with trend and seasonal components 

      Caivano, Michele; Harvey, Andrew; Luati, Alessandra (Springer, 2015-12-10)
      We describe observation driven time series models for Student-t and EGB2 conditional distributions in which the signal is a linear function of past values of the score of the conditional distribution. These specifications ...
    • Testing against changing correlation 

      Harvey, Andrew; Thiele, Stephen (Elsevier, 2015-09-25)
      A test for time-varying correlation is developed within the framework of a dynamic conditional score (DCS) model for both Gaussian and Student t-distributions. The test may be interpreted as a Lagrange multiplier test and ...
    • Time series models with an EGB2 conditional distribution 

      Caivano, Michele; Harvey, Andrew (2014-07-09)
      A time series model in which the signal is buried in noise that is non-Gaussian may throw up observations that, when judged by the Gaussian yardstick, are outliers. We describe an observation driven model, based on an ...
    • Time series models with an EGB2 conditional distribution 

      Harvey, Andrew; Caivano, Michele (Faculty of Economics, University of Cambridge, 2013-07-17)
      A time series model in which the signal is buried in noise that is non-Gaussian may throw up observations that, when judged by the Gaussian yardstick, are outliers. We describe an observation driven model, based on an ...
    • Two EGARCH models and one fat tail 

      Harvey, Andrew; Caivano, Michele (Faculty of Economics, University of Cambridge, 2013-07-29)
      We compare two EGARCH models which belong to a new class of models in which the dynamics are driven by the score of the conditional distribution of the observations. Models of this kind are called dynamic conditional score ...