Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models

By J. Keith Ord, Anne B. Koehler, Ralph D. Snyder. 1997

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Citations in the corpus

The Tourism Forecasting Competition (2011, background): “Ord et al. (1997), Hyndman et al. (2002) and Hyndman et al. (2005) (amongst others) have developed a statistical framework for the exponential smoothing methods presented in Table 1.”
25 years of time series forecasting (2006, methodology): “Ord et al. (1997) built on the work of Snyder (1985) by proposing a class of innovation state space models which can be considered as underlying some of the exponential smoothing methods.”
EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II (2006, unknown): “In response to these problems, Ord et al. (1997) built on the work of Snyder (1985) to create a general, yet remarkably simple class of state-space models with a single source of error (SSOE).”
25 Years of IIF Time Series Forecasting: A Selective Review (2005, methodology): “Ord et al. (1997) built on the work of Snyder (1985) by proposing a class of innovation state space models which can be considered as underlying some of the exponential smoothing methods.”
Forecasting and operational research: a review (2008, background): “…a new variant based on an alternative form, which has certain attractive theoretical features, is the so-called single source of error model (Ord et al, 1997; Hyndman et al, 2002), For damped trend smoothing this is given by Yt = lt−1 + bt−1 + t observation equation lt = lt−1 + bt−1 + t…”
Modelling and Forecasting Australian Domestic Tourism (2006, background): “Recently, a statistical framework for such forecasting methods has been developed (Hyndman et al., 2002; Ord et al., 1997).”
A General Framework for Observation Driven Time-Varying Parameter Models (2008, unknown): “Other frameworks for observation driven models within the exponential family of distributions have been suggested in the literature, including the generalized linear autoregressive (GLAR) models of Shephard (1995), the generalized autoregressive moving average (GARMA) models of Benjamin, Rigby, and ”
Prediction intervals for exponential smoothing using two new classes of state space models 30 January 2003 (2003, methodology): “Criteria such as maximum likelihood for selection of optimal estimates for the parameters can be found in Hyndman et al. (2001) and Ord et al. (1997).”
Holt–Winters Forecasting: An Alternative Formulation Applied to UK Air Passenger Data (2007, methodology): “In addition, recent developments provide a class of state space models for which exponential smoothing methods are optimal (Ord et al , 1997 ; Hyndman et al , 2002 ), this class contains both ARIMA models and dynamic nonlinear statistical models.”
The relationship between the Beveridge–Nelson decomposition and other permanent–transitory decompositions that are popular in economics (2008, methodology): “Proietti (2006) considered the UC-ARMA(2,1) with ρwv = 0 and θv free to see if expanding the dynamics of the cycle could give a UC model with uncorrelated components that matched the moments of the US real GDP data used by MNZ.”