Composite likelihood for time series models with a latent autoregressive process

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  1. Department of Statistics
  2. Composite likelihood for time series models with a latent autoregressive process

Composite likelihood for time series models with a latent autoregressive process

TitleComposite likelihood for time series models with a latent autoregressive process
Publication TypeJournal Article
Year of Publication2011
AuthorsNg, CT, Joe, H, Karlis, D, Liu, J
JournalStatistica Sinica
Volume21
Pagination279-305
Date PublishedJAN
AbstractConsistency and asymptotic normality properties are proved for various composite likelihood estimators in a time series model with a latent Gaussian autoregressive process. The proofs require different techniques than for clustered data with the number of clusters going to infinity. The composite likelihood estimation method is applied to a count time series consisting of daily car accidents with weather related covariates. A simulation study for the count time series model shows that the performance of composite likelihood estimator is better than Zeger's moment-based estimator, and the relative efficiency is high with respect to approximate maximum likelihood.
URLhttp://www3.stat.sinica.edu.tw/statistica/j21n1/J21N112/J21N112.html