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Modelling with the Novel INAR(1)-PTE Process

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Abstract

In this paper, the first-order non-negative integer-valued autoregressive process with Poisson-transmuted exponential innovations is introduced. Three estimation methods, namely, the conditional maximum likelihood, conditional least squares and Yule-Walker estimation methods are discussed to estimate the unknown parameters of the proposed process. Additionally, the simulation study is presented to assess the efficiencies of these estimation methods. Applications to two real-life data sets illustrate the usefulness of the proposed process.

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Authors and Affiliations

  1. Department of Mathematics, Bartin University, Bartin, Turkey

    Emrah Altun

  2. Department of Economics and Statistics, University of Mauritius, Moka, Mauritius

    Naushad Mamode Khan

Authors
  1. Emrah Altun
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  2. Naushad Mamode Khan
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Correspondence to Emrah Altun.

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Altun, E., Khan, N.M. Modelling with the Novel INAR(1)-PTE Process. Methodol Comput Appl Probab 24, 1735–1751 (2022). https://doi.org/10.1007/s11009-021-09878-2

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  • DOI: https://doi.org/10.1007/s11009-021-09878-2

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