A negative binomial model for time series of counts

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Count Time Series Models (2012, methodology): “In the context of negative binomial regression, the latent process model (38) has been extended by Davis and Wu (2009).”
QUASI‐LIKELIHOOD INFERENCE FOR NEGATIVE BINOMIAL TIME SERIES MODELS (2013, methodology): “In particular, we study observation-driven models, as opposed to Davis and Wu (2009) who studied parameter-driven models (for a classification between observation- and parameter-driven models, see Cox, 1981).”
Count Time Series: A Methodological Review (2021, background): “Details can be found in Davis, Dunsmuir, and Wang (2000) and Davis and Wu (2009).”
Some recent progress in count time series (2011, background): “See Harvey and Fernandes [15], MacDonald and Zucchini [16] and Davis and Wu [17], among other authors.”
Absolute regularity and ergodicity of Poisson count processes (2011, background): “There are possible applications in various fields, such as biometrics, econometrics and finance; see Davis, Dunsmuir and Wang [9] and Davis and Wu [10] for examples.”
A negative binomial integer‐valued GARCH model (2011, methodology): “Other authors who considered this set of data include Benjamin et al. (2003), Heinen (2003) and Davis and Wu (2009).”
Interventions in log-linear Poisson autoregression (2012, background): “6.1) and more recently Zheng et al. (2006) and Davis and Wu (2009).”