Binary time series models driven by a latent process
Binary time series models driven by a latent process
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DOI：
10.1016/j.ecosta.2017.02.001
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发表时间：
2017-04-01
影响因子：
1.9
通讯作者：
Moysiadis, Theodoros
中科院分区：
文献类型：
--
作者：
Fokianos, Konstantinos;Moysiadis, Theodoros
研究了包含潜在过程的二元时间序列模型的遍历性、平稳性和最大似然估计问题。考虑通用模型，覆盖不同规格的链接功能。讨论了最大似然估计，并证明了MLE满足标准渐近理论。通常用于分析二进制时间序列数据的逻辑和概率模型在本研究中特别重要。结果应用于模拟和实际数据。 (c) 2017 年 EcoSta 计量经济学和统计。由 Elsevier B.V. 出版。保留所有权利。
The problem of ergodicity, stationarity and maximum likelihood estimation is studied for binary time series models that include a latent process. General models are considered, covered by different specifications of a link function. Maximum likelihood estimation is discussed and it is shown that the MLE satisfies standard asymptotic theory. The logistic and probit models, routinely employed for the analysis of binary time series data, are of special importance in this study. The results are applied to simulated and real data. (c) 2017 EcoSta Econometrics and Statistics. Published by Elsevier B.V. All rights reserved.