Under-reported time-varying MINAR(1) process for modeling multivariate count series

Zeynab Aghabazaz and Iraj Kazemi

Computational Statistics & Data Analysis, 2023, vol. 188, issue C

Abstract: A time-varying multivariate integer-valued autoregressive of order one (tvMINAR(1)) model is introduced for the non-stationary time series of correlated counts when under-reporting is likely present. A non-diagonal autoregression probability network is structured to preserve the cross-correlation of multivariate series, provide a necessary condition to ease model-fittings computations, and derive the full likelihood using the Viterbi algorithm. The motivating construction applies to fully under-reported counts that rely on a mixture presentation of the random thinning operator. Simulation studies are conducted to examine the proposed model, and the analysis of COVID-19 daily cases is accomplished to highlight its usefulness in applications. Finally, the comparison of models is presented using the posterior predictive checking method.

Keywords: Binomial thinning operator; Cross-correlated time series; Forecasting; Random network model; Time-varying stochastic process (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:188:y:2023:i:c:s0167947323001366

DOI: 10.1016/j.csda.2023.107825

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