Integer-valued autoregressive models for counts showing underdispersion

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Zero‐Modified Geometric INAR(1) Process for Modelling Count Time Series with Deflation or Inflation of Zeros (2015, background): “…appeared in the literature dealing with INAR processes are those of Barczy et al. (2012), Nastić (2012), Nastić and Ristić (2012), Ristić et al. (2013), Weiß (2013), Weiß and Kim (2013), Meintanis and Karlis (2014), Schweer and Weiß (2014) and Bisaglia and Canale (2015) just to name a few.”
Underdispersion models: Models that are “under the radar” (2017, methodology): “Noting that the resulting dataset is underdispersed, Weiss (2013) considers models including Poisson and RGP to describe the data distribution.”
Extended Poisson INAR(1) processes with equidispersion, underdispersion and overdispersion (2017, background): “Weiß [41] studied a stationary INAR(1) process with Good and certain types of power law weighted Poisson innovations.”
An INAR(1) process for modeling count time series with equidispersion, underdispersion and overdispersion (2017, unknown): “Following this path, Schweer andWeiß (2014) investigated an INAR(1) process with compound Poisson innovations (and, hence, compound Poisson observations), which is suitable for modeling time series with overdispersion, while the INAR(1) model considered by Weiß (2013) exhibits underdispersion.”