Home > Research > Publications & Outputs > Estimation and testing linearity for non-linear...

Associated organisational unit

Links

Text available via DOI:

View graph of relations

Estimation and testing linearity for non-linear mixed Poisson autoregressions

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>2015
<mark>Journal</mark>Electronic Journal of Statistics
Issue number1
Volume9
Number of pages21
Pages (from-to)1357-1377
Publication statusPublished
Original languageEnglish

Abstract

Non-linear mixed Poisson autoregressive models are studied for the analysis of count time series. Given a correct mean specification of the model, we discuss quasi maximum likelihood estimation based on Poisson log-likelihood function. A score testing procedure for checking linearity of the mean process is developed. We consider the cases of identifiable and non identifiable parameters under the null hypothesis. When the parameters are identifiable then a chi-square approximation to the distribution of the score test is obtained. In the case of non identifiable parameters, a supremum score type test statistic is employed for checking linearity of the mean process. The methodology is applied to simulated and real data.