Modeling nonlinear time series with local mixtures of generalized linear models

Alexandre X. Carvalho*, Martin A. Tanner

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

The authors consider a novel class of nonlinear time series models based on local mixtures of regressions of exponential family models, where the covariates include functions of lags of the dependent variable. They give conditions to guarantee consistency of the maximum likelihood estimator for correctly specified models, with stationary and nonstationary predictors. They show that consistency of the maximum likelihood estimator still holds under model misspecification. They also provide probabilistic results for the proposed model when the vector of predictors contains only lags of transformations of the modeled time series. They illustrate the consistency of the maximum likelihood estimator and the probabilistic properties via Monte Carlo simulations. Finally, they present an application using real data.

Original languageEnglish (US)
Pages (from-to)97-113
Number of pages17
JournalCanadian Journal of Statistics
Volume33
Issue number1
DOIs
StatePublished - Mar 2005

Keywords

  • Generalized linear models
  • Mixtures-of-experts
  • Nonlinear time series

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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