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Similarity-based model for ordered categorical data

Author

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  • Gabi Gayer
  • Offer Lieberman
  • Omer Yaffe

Abstract

In a large variety of applications, the data for a variable we wish to explain are ordered and categorical. In this paper, we present a new similarity-based model for the scenario and investigate its properties. We establish that the process is ψ-mixing and strictly stationary and derive the explicit form of the autocorrelation function in some special cases. Consistency and asymptotic normality of the maximum likelihood estimator of the model’s parameters are proven. A simulation study supports our findings. The results are applied to the Netflix data set, comprised of a survey on users’ grading of movies.

Suggested Citation

  • Gabi Gayer & Offer Lieberman & Omer Yaffe, 2019. "Similarity-based model for ordered categorical data," Econometric Reviews, Taylor & Francis Journals, vol. 38(3), pages 263-278, March.
    • Handle: RePEc:taf:emetrv:v:38:y:2019:i:3:p:263-278
    DOI: 10.1080/07474938.2017.1308054
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    Cited by:

    1. Rossi, Francesca & Lieberman, Offer, 2023. "Spatial autoregressions with an extended parameter space and similarity-based weights," Journal of Econometrics, Elsevier, vol. 235(2), pages 1770-1798.

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