期刊
BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS
卷 33, 期 4, 页码 756-781出版社
BRAZILIAN STATISTICAL ASSOCIATION
DOI: 10.1214/19-BJPS437
关键词
Observation driven model; parameter driven model; autoregressive processes; Bayesian inference
资金
- FAPERJ
- FAPEMIG
- CNPq/Brazil
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据