@article { ISI:000287434900013,
	title = {Composite likelihood for time series models with a latent autoregressive process},
	journal = {Statistica Sinica},
	volume = {21},
	number = {1, SI},
	year = {2011},
	month = {JAN},
	pages = {279-305},
	publisher = {{Statistica Sinica, TAIWAN},
	abstract = {Consistency 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{\textquoteright}s moment-based estimator, and the relative efficiency is high with respect to approximate maximum likelihood.},
	url = {http://www3.stat.sinica.edu.tw/statistica/j21n1/J21N112/J21N112.html},
	author = {Ng, C. T. and Joe, Harry and Karlis, Dimitris and Liu, Juxin}
}
