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Tabela de conteúdos
Bayesian Dynamic Models - Ricardo Ehlers
Abstract
Dynamic models also known as state space models are formulated to allow for changes in the parameter values along time and have been used to the analysis and forecast of time series and space-time processes. A dynamic model may be specified by the following pair of equations,
In this project, the main concepts associated to dynamic modelling will be studied and applied to real time series data. The R package dlm estimates several models in this class.
Participants
- Ricardo Sandes Ehlers, UFPR
- Luiz Ledo Mota Melo Jr, UFRJ
- Iranei Claudio, UFPR
To Do List
- Write and test R functions to estimate normal DLM.
- Intervention and monitoring forecasting errors.
- Compare results in package dlm with the above functions.
- Write stochastic volatility model in DLM form.
Some references
- Migon, H. S., Gamerman, D., Lopes, H. F. and Ferreira, M. A. R. (2005), Bayesian Dynamic Models, In Handbook of Statistics, Volume 25: Bayesian Thinking, Modeling and Computation, pp. 553-588, (Editors Dey, D. and Rao, C.R.), Elsevier, Amsterdam.
- Giovanni Petris (2006). dlm: an R package for Bayesian analysis of Dynamic Linear Models.
