Binary time series models driven by a latent process

Fokianos, K. and Moysiadis, T. (2017) Binary time series models driven by a latent process. Econometrics and Statistics, 2. pp. 117-130.

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Abstract

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.

Item Type:
Journal Article
Journal or Publication Title:
Econometrics and Statistics
Subjects:
ID Code:
127723
Deposited By:
Deposited On:
26 Sep 2018 10:58
Refereed?:
Yes
Published?:
Published
Last Modified:
22 Nov 2020 05:56