A PARAMETER‐DRIVEN LOGIT REGRESSION MODEL FOR BINARY TIME SERIES

DOI10.1111/jtsa.12076zbMath1311.62107OpenAlexW1557567585MaRDI QIDQ5176851

Rongning Wu, Yunwei Cui

Publication date: 4 March 2015

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/jtsa.12076

zbMATH Keywords

regression analysisautocorrelationvariance estimationgeneralized linear modellatent processnon-stationaritykernel-based methodbinary time series

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Generalized linear models (logistic models) (62J12)

Related Items (2)

Marginal Estimation of Parameter Driven Binomial Time Series Models ⋮ On categorical time series models with covariates

Cites Work

• Unnamed Item
• Longitudinal data analysis using generalized linear models
• On variance estimation in a negative binomial time series regression model
• Regression models for binary time series with gaps
• M-estimation for autoregression with infinite variance
• Nonparametric estimation equations for time series data.
• Robust estimates in generalized partially linear single-index models
• Optimal smoothing in single-index models
• Dynamic probit models and financial variables in recession forecasting
• Markov Regression Models for Time Series: A Quasi-Likelihood Approach
• A negative binomial model for time series of counts
• Conditional inference in linear versus nonlinear models for binary time series
• Semiparametric Regression for Clustered Data Using Generalized Estimating Equations
• On autocorrelation in a Poisson regression model
• Partial Likelihood Inference For Time Series Following Generalized Linear Models

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