Coherent Forecasting for a Mixed Integer-Valued Time Series Model

Wooi Chen Khoo (), Seng Huat Ong and Biswas Atanu
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Wooi Chen Khoo: Department of Applied Statistics, School of Mathematical Sciences, Sunway University, Subang Jaya 47500, Malaysia
Seng Huat Ong: Institute of Actuarial Science and Data Analytics, UCSI University, Kuala Lumpur 56000, Malaysia
Biswas Atanu: Applied Statistics Unit, Indian Statistical Institute, 203 B.T Road, Kolkata 700108, India

Mathematics, 2022, vol. 10, issue 16, 1-15

Abstract: In commerce, economics, engineering and the sciences, quantitative methods based on statistical models for forecasting are very useful tools for prediction and decision. There is an abundance of papers on forecasting for continuous-time series but relatively fewer papers for time series of counts which require special consideration due to the integer nature of the data. A popular method for modelling is the method of mixtures which is known for its flexibility and thus improved prediction capability. This paper studies the coherent forecasting for a flexible stationary mixture of Pegram and thinning (MPT) process, and develops the likelihood-based asymptotic distribution. Score functions and the Fisher information matrix are presented. Numerical studies are used to assess the performance of the forecasting methods. Also, a comparison is made with existing discrete-valued time series models. Finally, the practical application is illustrated with two sets of real data. It is shown that the mixture model provides good forecasting performance.

Keywords: asymptotic distribution; coherent forecasting; INAR(1); mixture; Pegram operator; binomial thinning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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