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Generalized discrete autoregressive moving‐average models

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  • Tobias A. Möller
  • Christian H. Weiß

Abstract

This article proposes the generalized discrete autoregressive moving‐average (GDARMA) model as a parsimonious and universally applicable approach for stationary univariate or multivariate time series. The GDARMA model can be applied to any type of quantitative time series. It allows to compute moment properties in a unique way, and it exhibits the autocorrelation structure of the traditional ARMA model. This great flexibility is obtained by using data‐specific variation operators, which is illustrated for the most common types of time series data, such as counts, integers, reals, and compositional data. The practical potential of the GDARMA approach is demonstrated by considering a time series of integers regarding votes for a change of the interest rate, and a time series of compositional data regarding television market shares.

Suggested Citation

  • Tobias A. Möller & Christian H. Weiß, 2020. "Generalized discrete autoregressive moving‐average models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(4), pages 641-659, July.
    • Handle: RePEc:wly:apsmbi:v:36:y:2020:i:4:p:641-659
    DOI: 10.1002/asmb.2520
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    References listed on IDEAS

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    1. Sirchenko Andrei, 2020. "A model for ordinal responses with heterogeneous status quo outcomes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(1), pages 1-16, February.
    2. Christian Weiß & Rainer Göb, 2008. "Measuring serial dependence in categorical time series," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 71-89, February.
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