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

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 m...

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Bibliographic Details
Published inTest (Madrid, Spain) Vol. 32; no. 4; pp. 1195 - 1229
Main Authors Shamma, Nisreen, Mohammadpour, Mehrnaz, Shirozhan, Masoumeh
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2023
Springer Nature B.V
Subjects
Autoregressive models
Economics
Finance
Insurance
Least squares
Management
Mathematics and Statistics
Maximum likelihood estimation
Modelling
Nonlinear analysis
Original Paper
Parameter estimation
Search algorithms
Simulation models
Statistical Theory and Methods
Statistics
Statistics for Business
Time series
62M10
62P10
False position method
Dependent counting series
D-NeSS algorithm
Integer-valued threshold autoregressive model
Min-Min algorithm
60G25
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Summary: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.
ISSN:1133-0686
1863-8260
DOI:10.1007/s11749-023-00869-8