Time series analysis of non‐Gaussian observations based on state space models from both classical and Bayesian perspectives

By J. Durbin, S. J. Koopman. 1998

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Citations in the corpus

Effective Bayesian Modeling of Groups of Related Count Time Series (2014, background): “Durbin & Koopman (2000) present an alternative approach based on importance sampling and antithetic variables.”
Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (2009, unknown): “This data set has been analysed by Durbin and Koopman (2000), among others.”
Forecasting Daily Variability of the S&P 100 Stock Index Using Historical, Realised and Implied Volatility Measurements (2005, methodology): “Further details of this approach are discussed in Durbin and Koopman (2000).”
SV mixture models with application to S&P 500 index returns ☆ (2007, unknown): “These techniques are closely related to ideas used in Durbin and Koopman (1997, 2000), Sandmann and Koopman (1998), Shephard and Pitt (1997), and Liesenfeld and Richard (2003).”
Time aggregation and the hodrick-prescott filter (2001, methodology): “(This is similar to approximating spectral densities by preserving the mode, see for example Durbin and Koopman (2000)).”
On the Relationship between the Conditional Mean and Volatility of Stock Returns: A Latent VAR Approach (2004, methodology): “To make matters worse, our estimator suffers from the critique of Shephard (2000), that the ratio of densities wðy; cÞ may not have a finite variance.”
Stochastic model specification search for Gaussian and partial non-Gaussian state space models (2010, methodology): “Durbin and Koopman (2001). MCMC draws for ±θi are obtained by multiplying the square root of the MCMC draws θ (m) i with a random sign.”
Theory & Methods: Estimation of the Stochastic Volatility Model by the Empirical Characteristic Function Method (2002, unknown): “…methods, such as the simulated maximum likelihood proposed by Danielsson (1994), and Markov chain Monte Carlo (MCMC) proposed by Jacquier, Polson & Rossi (1994) and Kim et al. (1998), and the simulation method using important sampling and antithetic variables proposed by Durbin & Koopman (2000).”