Measures of kurtosis and skewness of INGARCH model
Recently there has been a growing interest in time series of counts/integer-valued time series. The time series under the hypothesis of homogeneous variance becomes unrealistic in many situations because the variance tend to change with level. Important models such as ACP (autoregressive condition...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
American Institute of Physics Inc.
2014
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/49911/1/49911_Measures%20of%20kurtosis%20and%20skewness%20of%20INGARCH%20model_SCOPUS.pdf http://irep.iium.edu.my/49911/3/Measures%20of%20kurtosis%20and%20skewness%20of%20INGARCH%20model.pdf http://irep.iium.edu.my/49911/ http://aip.scitation.org/toc/apc/1605/1?windowStart=150&size=50&expanded=1605 |
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| Summary: | Recently there has been a growing interest in time series of counts/integer-valued time series. The time series
under the hypothesis of homogeneous variance becomes unrealistic in many situations because the variance tend to
change with level. Important models such as ACP (autoregressive conditional Poisson ) models and integer valued
GARCH models have been proposed in the literature. Ghahramani and Thavaneswaran [1] studied the moment
properties of ACP models using martingale transformation. However the forecasting for count process has not been
studied in the literature. Using a martingale transformation, Thavaneswaran et al. [2] studied the volatility forecasts for
GARCH models. In this paper, first we derive closed form expressions for skewness and kurtosis for count processes via
martingale transformation then we study the joint forecasts for integer-valued count models with errors following
Poisson. |
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