A goodness-of-fit test for integer-valued autoregressive processes
For autoregressive count data time series, a goodness-of-fit test based on the empirical joint probability generating function is considered. The underlying process is contained in a general class of Markovian models satisfying a drift condition. Asymptotic theory for the test statistic is provided,...
Gespeichert in:
| 1. Verfasser: | |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
January 2016
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| In: |
Journal of time series analysis
Year: 2015, Jahrgang: 37, Heft: 1, Pages: 77-98 |
| ISSN: | 1467-9892 |
| DOI: | 10.1111/jtsa.12138 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1111/jtsa.12138 Verlag, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1111/jtsa.12138 
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| Verfasserangaben: | Sebastian Schweer |
MARC
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| 520 | |a For autoregressive count data time series, a goodness-of-fit test based on the empirical joint probability generating function is considered. The underlying process is contained in a general class of Markovian models satisfying a drift condition. Asymptotic theory for the test statistic is provided, including a functional central limit theorem for the non-parametric estimation of the stationary distribution and a parametric bootstrap method. Connections between the new approach and existing tests for count data time series based on moment estimators appear in limiting scenarios. Finally, the test is applied to a real data set. | ||
| 534 | |c 2015 | ||
| 650 | 4 | |a drift condition | |
| 650 | 4 | |a Goodness-of-fit test | |
| 650 | 4 | |a integer-valued time series | |
| 650 | 4 | |a parametric bootstrap | |
| 650 | 4 | |a probability generating function | |
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