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On binary and categorical time series models with feedback

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  • Moysiadis, Theodoros
  • Fokianos, Konstantinos

Abstract

We study the problem of ergodicity, stationarity and maximum likelihood estimation for multinomial logistic models that include a latent process. Our work includes various models that have been proposed for the analysis of binary and, more general, categorical time series. We give verifiable ergodicity and stationarity conditions for the analysis of such time series data. In addition, we study maximum likelihood estimation and prove that, under mild conditions, the estimator is asymptotically normally distributed. These results are applied to real and simulated data.

Suggested Citation

  • Moysiadis, Theodoros & Fokianos, Konstantinos, 2014. "On binary and categorical time series models with feedback," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 209-228.
  • Handle: RePEc:eee:jmvana:v:131:y:2014:i:c:p:209-228
    DOI: 10.1016/j.jmva.2014.07.004
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    References listed on IDEAS

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    Cited by:

    1. Park, Byeong U. & Simar, Léopold & Zelenyuk, Valentin, 2017. "Nonparametric estimation of dynamic discrete choice models for time series data," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 97-120.
    2. Fokianos, Konstantinos & Moysiadis, Theodoros, 2017. "Binary time series models driven by a latent process," Econometrics and Statistics, Elsevier, vol. 2(C), pages 117-130.
    3. repec:bla:jtsera:v:38:y:2017:i:6:p:880-894 is not listed on IDEAS

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