Modeling vector nonlinear time series using POLYMARS

Authors
Publication date 2003
Journal Computational Statistics and Data Analysis
Volume | Issue number 42
Pages (from-to) 73-90
Number of pages 17
Organisations
  • Faculty of Economics and Business (FEB) - Amsterdam School of Economics Research Institute (ASE-RI)
Abstract A modified multivariate adaptive regression splines method for modeling vector nonlinear time series is investigated. The method results in models that can capture certain types of vector self-exciting threshold autoregressive behavior, as well as provide good predictions for more general vector nonlinear time series. The effect of different model selection criteria on fitted models and predictions is evaluated through simulation. The method is illustrated for a real data example, to model a series of intra-day electricity loads in two neighboring Australian states.
Document type Article
Published at https://doi.org/10.1016/S0167-9473(02)00123-8
Published at http://www1.fee.uva.nl/pp/bin/refereedjournalpublication1466fulltext.pdf
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