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Testing for serial dependence in time series models of counts
Author(s) -
Jung Robert C.,
Tremayne A. R.
Publication year - 2003
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/1467-9892.00293
Subject(s) - mathematics , autoregressive model , series (stratigraphy) , statistics , binomial (polynomial) , autocorrelation , sample (material) , sample size determination , order of integration (calculus) , econometrics , time series , negative binomial distribution , statistical hypothesis testing , integer (computer science) , computer science , mathematical analysis , paleontology , chemistry , chromatography , poisson distribution , biology , programming language
. 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.