Nonlinear and Non-Gaussian State-Space Modeling with Monte-Carlo Simulations

Nonlinear and Non-Gaussian State-Space Modeling with Monte-Carlo Simulations

We propose two nonlinear and nonnormal filters based on Monte Carlo simulation techniques. In terms of programming and computational requirements both filters are more tractable than other nonlinear filters that use numerical integration, Monte Carlo integration with importance sampling or Gibbs sam...

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Main Authors: Mariano, Roberto S., Tanizaki, Hisashi
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 1998
Subjects:
Economics
Online Access:https://ink.library.smu.edu.sg/soe_research/272
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Institution: Singapore Management University
Language: English
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spelling sg-smu-ink.soe_research-12712010-09-23T05:48:03Z Nonlinear and Non-Gaussian State-Space Modeling with Monte-Carlo Simulations Mariano, Roberto S. Tanizaki, Hisashi We propose two nonlinear and nonnormal filters based on Monte Carlo simulation techniques. In terms of programming and computational requirements both filters are more tractable than other nonlinear filters that use numerical integration, Monte Carlo integration with importance sampling or Gibbs sampling. The proposed filters are extended to prediction and smoothing algorithms. Monte Carlo experiments are carried out to assess the statistical merits of the proposed filters. 1998-01-01T08:00:00Z text https://ink.library.smu.edu.sg/soe_research/272 info:doi/10.1016/s0304-4076(97)80226-6 Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Economics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Economics
spellingShingle Economics
Mariano, Roberto S.
Tanizaki, Hisashi
Nonlinear and Non-Gaussian State-Space Modeling with Monte-Carlo Simulations
description We propose two nonlinear and nonnormal filters based on Monte Carlo simulation techniques. In terms of programming and computational requirements both filters are more tractable than other nonlinear filters that use numerical integration, Monte Carlo integration with importance sampling or Gibbs sampling. The proposed filters are extended to prediction and smoothing algorithms. Monte Carlo experiments are carried out to assess the statistical merits of the proposed filters.
format text
author Mariano, Roberto S.
Tanizaki, Hisashi
author_facet Mariano, Roberto S.
Tanizaki, Hisashi
author_sort Mariano, Roberto S.
title Nonlinear and Non-Gaussian State-Space Modeling with Monte-Carlo Simulations
title_short Nonlinear and Non-Gaussian State-Space Modeling with Monte-Carlo Simulations
title_full Nonlinear and Non-Gaussian State-Space Modeling with Monte-Carlo Simulations
title_fullStr Nonlinear and Non-Gaussian State-Space Modeling with Monte-Carlo Simulations
title_full_unstemmed Nonlinear and Non-Gaussian State-Space Modeling with Monte-Carlo Simulations
title_sort nonlinear and non-gaussian state-space modeling with monte-carlo simulations
publisher Institutional Knowledge at Singapore Management University
publishDate 1998
url https://ink.library.smu.edu.sg/soe_research/272
_version_ 1770569094737690624

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