Marginal analysis of count time series in the presence of missing observations

Simon Nik ()
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Simon Nik: Helmut Schmidt University

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2024, vol. 33, issue 4, No 6, 1105-1128

Abstract: Abstract Time series in real-world applications often have missing observations, making typical analytical methods unsuitable. One method for dealing with missing data is the concept of amplitude modulation. While this principle works with any data, here, missing data for unbounded and bounded count time series are investigated, where tailor-made dispersion and skewness statistics are used for model diagnostics. General closed-form asymptotic formulas are derived for such statistics with only weak assumptions on the underlying process. Moreover, closed-form formulas are derived for the popular special cases of Poisson and binomial autoregressive processes, always under the assumption that missingness occurs. The finite-sample performances of the considered asymptotic approximations are analyzed with simulations. The practical application of the corresponding dispersion and skewness tests under missing data is demonstrated with three real data examples.

Keywords: Amplitude modulation; Dispersion index; Skewness index; Missing data; Poisson autoregessive model; Binomial autoregessive model (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11749-024-00938-6

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