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November 2019 Time series of count data: A review, empirical comparisons and data analysis
Glaura C. Franco, Helio S. Migon, Marcos O. Prates
Braz. J. Probab. Stat. 33(4): 756-781 (November 2019). DOI: 10.1214/19-BJPS437

Abstract

Observation and parameter driven models are commonly used in the literature to analyse time series of counts. In this paper, we study the characteristics of a variety of models and point out the main differences and similarities among these procedures, concerning parameter estimation, model fitting and forecasting. Alternatively to the literature, all inference was performed under the Bayesian paradigm. The models are fitted with a latent AR($p$) process in the mean, which accounts for autocorrelation in the data. An extensive simulation study shows that the estimates for the covariate parameters are remarkably similar across the different models. However, estimates for autoregressive coefficients and forecasts of future values depend heavily on the underlying process which generates the data. A real data set of bankruptcy in the United States is also analysed.

Citation

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Glaura C. Franco. Helio S. Migon. Marcos O. Prates. "Time series of count data: A review, empirical comparisons and data analysis." Braz. J. Probab. Stat. 33 (4) 756 - 781, November 2019. https://doi.org/10.1214/19-BJPS437

Information

Received: 1 May 2018; Accepted: 1 March 2019; Published: November 2019
First available in Project Euclid: 26 August 2019

zbMATH: 07120733
MathSciNet: MR3996316
Digital Object Identifier: 10.1214/19-BJPS437

Rights: Copyright © 2019 Brazilian Statistical Association

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Vol.33 • No. 4 • November 2019
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