A threshold modeling for nonlinear time series of counts: application to COVID-19 data

Nisreen Shamma, Mehrnaz Mohammadpour () and Masoumeh Shirozhan
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Nisreen Shamma: University of Mazandaran
Mehrnaz Mohammadpour: University of Mazandaran
Masoumeh Shirozhan: Mazandaran Province

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2023, vol. 32, issue 4, No 7, 1195-1229

Abstract: Abstract This article studies a threshold autoregressive model with the dependent thinning structure for modeling nonlinear time series of counts. Some properties are derived for the model and two approaches in estimation are applied, the modified conditional least square and conditional maximum likelihood methods which are adjusted by the Min-Min algorithm. The unknown threshold parameter is estimated using the nested sub-sample search algorithm and the minimum of maximized log-likelihood function methods. The efficiency of the estimators is evaluated using a simulation study. The application of the model is discussed on the COVID-19 data set.

Keywords: Dependent counting series; False position method; Integer-valued threshold autoregressive model; Min-Min algorithm; D-NeSS algorithm; 62P10; 62M10; 60G25 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11749-023-00869-8

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