Abstract
This paper introduces the Pre-Smoothed Particle Filter (PSPF) as a regularized alternative to the Sampling Importance Resampling (SIR) filter for non-linear state space models with additive Gaussian observation noise. The PSPF adaptively bridges the unbiased, but variance-prone, SIR filter and the biased, but less variance-prone Gaussian update filter. Our main contribution is the adaptive determination of the degree of smoothing in each time step. We illustrate the approach using examples from econometrics, namely a continuous time short term interest rate model and a dynamic stochastic general equilibrium model fitted to noisy data. The PSPF is shown to be highly suited for dynamic models with high signal-to-noise ratio, for which the SIR filter has problems.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
