Online data processing: comparison of Bayesian regularized particle filters
AbstractThe aim of this paper is to compare three regularized particle filters in an online data processing context. We carry out the comparison in terms of hidden states filtering and parameters estmation, considering a Bayesian paradigm and a univariate stochastic volatility model. We discuss the use of an improper prior distribution in the initialization of the filtering procedure and show that the Regularized Auxiliary Particle Filter (R-APF) outperforms the Regularized Sequential Importance Sampling (R-SIS) and the Regularized Sampling Importance Resampling (R-SIR).
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Bibliographic InfoPaper provided by University of Brescia, Department of Economics in its series Working Papers with number 0703.
Date of creation: 2007
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