Nonlinear and non-Gaussian state-space modeling with Monte Carlo simulations
Journal of Econometrics, ISSN: 0304-4076, Vol: 83, Issue: 1, Page: 263-290
1998
- 54Citations
- 20Usage
- 33Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations54
- Citation Indexes54
- 54
- CrossRef48
- Usage20
- Abstract Views20
- Captures33
- Readers33
- 33
Article 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.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0304407697802266; http://dx.doi.org/10.1016/s0304-4076(97)80226-6; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=0001359360&origin=inward; http://linkinghub.elsevier.com/retrieve/pii/S0304407697802266; http://api.elsevier.com/content/article/PII:S0304407697802266?httpAccept=text/xml; http://api.elsevier.com/content/article/PII:S0304407697802266?httpAccept=text/plain; https://linkinghub.elsevier.com/retrieve/pii/S0304407697802266; https://ink.library.smu.edu.sg/soe_research/272; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1271&context=soe_research; http://dx.doi.org/10.1016/s0304-4076%2897%2980226-6
Elsevier BV
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