Open Access
2021 Time-varying auto-regressive models for count time-series
Arkaprava Roy, Sayar Karmakar
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Electron. J. Statist. 15(1): 2905-2938 (2021). DOI: 10.1214/21-EJS1851

Abstract

Count-valued time series data are routinely collected in many application areas. We are particularly motivated to study the count time series of daily new cases, arising from the COVID-19 spread. First, we propose a Bayesian framework to study the time-varying semiparametric AR(p) model for the count and then extend it to a more sophisticated time-varying INGARCH model. We calculate posterior contraction rates of the proposed Bayesian methods with respect to the average Hellinger metric. Our proposed structures of the models are amenable to Hamiltonian Monte Carlo (HMC) sampling for efficient computation. We substantiate our methods by simulations that show superiority compared to some of the existing methods that closely fit this setting. Finally, we analyze the daily time series data of newly confirmed cases in NYC to study the spread of COVID for three months.

Citation

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Arkaprava Roy. Sayar Karmakar. "Time-varying auto-regressive models for count time-series." Electron. J. Statist. 15 (1) 2905 - 2938, 2021. https://doi.org/10.1214/21-EJS1851

Information

Received: 1 March 2021; Published: 2021
First available in Project Euclid: 28 May 2021

Digital Object Identifier: 10.1214/21-EJS1851

Keywords: autoregressive model , B-splines , count-valued time series , Covid-19 , Hamiltonian Monte Carlo (HMC) , INGARCH , non-stationary , Poisson regression , Posterior contraction rates

Vol.15 • No. 1 • 2021
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