A General Framework for Observation Driven Time-Varying Parameter Models

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Count Time Series Models (2012, background): “…models for count time series; see Zeger and Qaqish (1988), Li (1994), MacDonald and Zucchini (1997), Brumback et al. (2000), Kedem and Fokianos (2002), Benjamin et al. (2003), Davis et al. (2003), Fokianos and Kedem (2004), Jung et al. (2006), Creal et al. (2008) and Fokianos and Tjøstheim (2011).”
A new time-varying optimal copula model identifying the dependence across markets (2017, background): “Similarly, Creal et al. (2008) assumed that the parameters follow a generalized autoregressive score (GAS) model. Giacomini et al. (2009) proposed estimating the time-varying parameters adaptively by means of local parametric fitting. Hafner and Reznikova (2010) proposed a semiparametric approach to”
Time‐Varying Transition Probabilities for Markov Regime Switching Models (2017, methodology): “Similar developments for a linear Gaussian state space model have been reported by Creal et al. (2008) and Delle Monache and Petrella (2014) The remainder of the paper is organized as follows.”
Information Theoretic Optimality of Observation Driven Time Series Models (2014, unknown): “The main distinguishing feature of our model class is the use of the score function of the conditional (or the predictive) observation density to drive the changes of the parameters over time; see Creal, Koopman, and Lucas (2008, 2013) and Harvey (2013).”