Accelerating score-driven time series models

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.