Testing for serial dependence in time series models of counts
- 1 January 2003
- journal article
- Published by Wiley in Journal of Time Series Analysis
- Vol. 24 (1) , 65-84
- https://doi.org/10.1111/1467-9892.00293
Abstract
In analysing time series of counts, the need to test for the presence of a dependence structure routinely arises. Suitable tests for this purpose are considered in this paper. Their size and power properties are evaluated under various alternatives taken from the class of INARMA processes. We find that all the tests considered except one are robust against extra binomial variation in the data and that tests based on the sample autocorrelations and the sample partial autocorrelations can help to distinguish between integer‐valued first‐order and second‐order autoregressive as well as first‐order moving average processes.Keywords
All Related Versions
This publication has 10 references indexed in Scilit:
- Estimation in integer‐valued moving average modelsApplied Stochastic Models in Business and Industry, 2001
- Theory & Methods: Non‐Gaussian Conditional Linear AR(1) ModelsAustralian & New Zealand Journal of Statistics, 2000
- Regression Analysis of Count DataPublished by Cambridge University Press (CUP) ,1998
- THE INTEGER‐VALUED AUTOREGRESSIVE (INAR(p)) MODELJournal of Time Series Analysis, 1991
- An integer-valuedpth-order autoregressive structure (INAR(p)) processJournal of Applied Probability, 1990
- Goodness-of-fit for a branching process with immigration using sample partial autocorrelationsStochastic Processes and their Applications, 1989
- Integer-valued moving average (INMA) processStatistical Papers, 1988
- FIRST‐ORDER INTEGER‐VALUED AUTOREGRESSIVE (INAR(1)) PROCESSJournal of Time Series Analysis, 1987
- SOME ASPECTS OF THE PERFORMANCE OF DIAGNOSTIC CHECKS IN BIVARIATE TIME SERIES MODELSJournal of Time Series Analysis, 1986
- On a Test Whether Two Samples are from the Same PopulationThe Annals of Mathematical Statistics, 1940