Dynamic model for a first order autoregression process with Bayesian methodology
Abstract
A ramification of a first order autoregression process is provided. It comprises randomized and variant coefficients in time and assumes a structure of dependency of randomized coefficients that leads towards adapted Kalman's Filter. Although the Kalman Filter model is a generalization of the ordinary Kalman Filter, its analysis produces technical difficulties. It does not seem to be impossible to find a closed form for the filter. Monte Carlo's simulation was applied to Markov's Chain by Gibbs-Sampling and Metropolis-Hasting algorithms to infer parameters of model and work out forecasts of data for a time series of indexes of shares and meat prices.Downloads
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Published
2008-04-22
How to Cite
Mena, L., & Andrade Filho, M. G. de. (2008). Dynamic model for a first order autoregression process with Bayesian methodology. Acta Scientiarum. Technology, 24, 1755-1760. https://doi.org/10.4025/actascitechnol.v24i0.2553
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Section
Statistics
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2019CiteScore
36th percentile
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