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,...
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| Main Author: | |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
January 2016
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| In: |
Journal of time series analysis
Year: 2015, Volume: 37, Issue: 1, Pages: 77-98 |
| ISSN: | 1467-9892 |
| DOI: | 10.1111/jtsa.12138 |
| Online Access: | 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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| Author Notes: | Sebastian Schweer |
| Summary: | 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. |
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| Item Description: | First published: 08 June 2015 Gesehen am 01.06.2018 |
| Physical Description: | Online Resource |
| ISSN: | 1467-9892 |
| DOI: | 10.1111/jtsa.12138 |