Estimation of count time series model with varying frequencies: Application to prevalence rate of diseases
In modeling time series data with varying frequencies, variables at higher frequency are commonly aggregated first to coincide with the usually lower frequency of the dependent variable, and in the process, resulting to information loss. A semiparametric count model for time series data with varying...
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| Format: | text |
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Animo Repository
2019
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| Online Access: | https://animorepository.dlsu.edu.ph/faculty_research/11192 |
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| Institution: | De La Salle University |
| Summary: | In modeling time series data with varying frequencies, variables at higher frequency are commonly aggregated first to coincide with the usually lower frequency of the dependent variable, and in the process, resulting to information loss. A semiparametric count model for time series data with varying frequencies is proposed. High frequency covariates are incorporated into nonparametric functions (without aggregation) to explain behavior of poisson-distributed count response. The contribution of the covariate with same frequency as the response is assumed to be parametric. Simulation studies and real data application show advantages of the model based on the Mean Absolute Deviation (MAD) over a General Additive Model and an Ordinary Poisson regression model especially on covariates with weak or no autocorrelation. |
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