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Abstract
In this project we study problems of inference and forecasting in autoregressive models with periodic correlation from a Bayesian perspective. Normality and unimodality assumptions are rarely verified in practice and the usual approach is to try Box-Cox transformations to obtain approximate normality and stabilize the periodic variance. More recently, mixture models were developed to take into account asymmetry and multimodality.
Participants
- Ricardo Ehlers (UFPR)
- Marinho Gomes de Andrade (USP)
Some references
<bibtex> @Article{lewis,
author = {Lewis, P.A.W. and Ray, B.K.},
title = {Nonlinear Modelling of Periodic Threshold Autoregressions using TSMARS},
journal = {Journal of Time Series Analysis},
year = {2002},
volume = {23},
number = {4},
pages = {459-471}
} @Article{shao06,
author = {Shao, Q},
title = {Mixture Periodic Autoregressive Time Series Models},
journal = {Statistics and Probability Letters},
year = {2006},
volume = {76},
pages = {609-618},
} </bibtex>
