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A simple and efficient simulation smoother for state space time series analysis. (English) Zbl 1036.62071
Summary: A simulation smoother in state space time series analysis is a procedure for drawing samples from the conditional distribution of state or disturbance vectors given the observations. We present a new technique for this which is both simple and computationally efficient. The treatment includes models with diffuse initial conditions and regression effects. Computational comparisons are made with the previous standard method. Two applications are provided to illustrate the use of the simulation smoother for Gibbs sampling for Bayesian inference and importance sampling for classical inference.

MSC:
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
65C40 Numerical analysis or methods applied to Markov chains
62M20 Inference from stochastic processes and prediction
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