Accelerating score-driven time series models

F. Blasques, P. Gorgi, S. J. Koopman*

*Corresponding author for this work

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We propose a new class of score-driven time series models that allows for a more flexible weighting of score innovations for the filtering of time varying parameters. The parameter for the score innovation is made time-varying by means of an updating equation that accounts for the autocorrelations of past innovations. We provide the theoretical foundations for this acceleration method by showing optimality in terms of reducing Kullback–Leibler divergence. The empirical relevance of this accelerated score-driven updating method is illustrated in two empirical studies. First, we include acceleration in the generalized autoregressive conditional heteroskedasticity model. We adopt the new model to extract volatility from exchange rates and to analyze daily density forecasts of volatilities from all individual stock return series in the Standard & Poor's 500 index. Second, we consider a score-driven acceleration for the time-varying mean and use this new model in a forecasting study for US inflation.

Original languageEnglish
Pages (from-to)359-376
Number of pages18
JournalJournal of Econometrics
Issue number2
Early online date11 Apr 2019
Publication statusPublished - Oct 2019


  • GARCH models
  • Kullback–Leibler divergence
  • S&P 500 stocks
  • Score-driven models
  • Time-varying parameters
  • US inflation

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