Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/44960
Title: | Zero-truncated compound Poisson integer-valued GARCH models for time series | Authors: | Gonçalves, Esmeralda Mendes-Lopes, Nazaré |
Issue Date: | 2017 | Publisher: | Taylor & Francis | Project: | info:eu-repo/grantAgreement/FCT/5876/147205/PT | Serial title, monograph or event: | Statistics | Abstract: | Starting from the compound Poisson INGARCH models, we introduce in this paper a new family of integer-valued models suitable to describe count data without zeros that we name zero-truncated CP-INGARCH processes. For such class of models, a probabilistic study concerning moments existence, stationarity and ergodicity is developed. The conditional quasi-maximum likelihood method is introduced to consistently estimate the parameters of a wide zero-truncated compound Poisson subclass of models. The conditional maximum likelihood method is also used to estimate the parameters of ZTCP-INGARCH processes associated with well-specified conditional laws. A simulation study that compares some of those estimators and illustrates their finite distance behaviour as well as a real-data application conclude the paper. | URI: | https://hdl.handle.net/10316/44960 | DOI: | 10.1080/02331888.2017.1410154 10.1080/02331888.2017.1410154 |
Rights: | embargoedAccess |
Appears in Collections: | I&D CMUC - Artigos em Revistas Internacionais |
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Statistics_2017_ZeroTruncated_G_ML.pdf | 527.69 kB | Adobe PDF | View/Open |
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