Bayesian estimation for novel geometric INGARCH model
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911017311666176 |
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| author | Andrews, Divya Kuttenchalil Balakrishna, N. |
| author_facet | Andrews, Divya Kuttenchalil Balakrishna, N. |
| contents | This paper introduces an integer-valued generalized autoregressive conditional heteroskedasticity (INGARCH) model based on the novel geometric distribution and discusses some of its properties. The parameter estimation problem of the models are studied by conditional maximum likelihood and Bayesian approach using Hamiltonian Monte Carlo (HMC) algorithm. The results of the simulation studies and real data analysis affirm the good performance of the estimators and the model. Forecasting using the Bayesian predictive distribution has also been studied and evaluated using real data analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01283 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bayesian estimation for novel geometric INGARCH model Andrews, Divya Kuttenchalil Balakrishna, N. Methodology Computation This paper introduces an integer-valued generalized autoregressive conditional heteroskedasticity (INGARCH) model based on the novel geometric distribution and discusses some of its properties. The parameter estimation problem of the models are studied by conditional maximum likelihood and Bayesian approach using Hamiltonian Monte Carlo (HMC) algorithm. The results of the simulation studies and real data analysis affirm the good performance of the estimators and the model. Forecasting using the Bayesian predictive distribution has also been studied and evaluated using real data analysis. |
| title | Bayesian estimation for novel geometric INGARCH model |
| topic | Methodology Computation |
| url | https://arxiv.org/abs/2410.01283 |