Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -