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Binary time series models driven by a latent process

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Binary time series models driven by a latent process. / Fokianos, K.; Moysiadis, T.
In: Econometrics and Statistics, Vol. 2, 04.2017, p. 117-130.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Fokianos, K & Moysiadis, T 2017, 'Binary time series models driven by a latent process', Econometrics and Statistics, vol. 2, pp. 117-130. https://doi.org/10.1016/j.ecosta.2017.02.001

APA

Fokianos, K., & Moysiadis, T. (2017). Binary time series models driven by a latent process. Econometrics and Statistics, 2, 117-130. https://doi.org/10.1016/j.ecosta.2017.02.001

Vancouver

Fokianos K, Moysiadis T. Binary time series models driven by a latent process. Econometrics and Statistics. 2017 Apr;2:117-130. Epub 2017 Feb 21. doi: 10.1016/j.ecosta.2017.02.001

Author

Fokianos, K. ; Moysiadis, T. / Binary time series models driven by a latent process. In: Econometrics and Statistics. 2017 ; Vol. 2. pp. 117-130.

Bibtex

@article{6d040f16520d4a978dbc70e18b604aad,
title = "Binary time series models driven by a latent process",
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.",
keywords = "Autocorrelation, Generalized linear models, Logistic model, Probit model, Regression, Weak dependence",
author = "K. Fokianos and T. Moysiadis",
year = "2017",
month = apr,
doi = "10.1016/j.ecosta.2017.02.001",
language = "English",
volume = "2",
pages = "117--130",
journal = "Econometrics and Statistics",

}

RIS

TY - JOUR

T1 - Binary time series models driven by a latent process

AU - Fokianos, K.

AU - Moysiadis, T.

PY - 2017/4

Y1 - 2017/4

N2 - 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.

AB - 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.

KW - Autocorrelation

KW - Generalized linear models

KW - Logistic model

KW - Probit model

KW - Regression

KW - Weak dependence

U2 - 10.1016/j.ecosta.2017.02.001

DO - 10.1016/j.ecosta.2017.02.001

M3 - Journal article

VL - 2

SP - 117

EP - 130

JO - Econometrics and Statistics

JF - Econometrics and Statistics

ER -